File size: 94,086 Bytes
6fa4bc9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 |
{
"paper_id": "2019",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T07:30:12.966452Z"
},
"title": "Multi-linguality Helps: Event-Argument Extraction for Disaster Domain in Cross-lingual and Multi-lingual Setting",
"authors": [
{
"first": "Zishan",
"middle": [],
"last": "Ahmad",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Indian Institute of Technology",
"location": {
"addrLine": "Patna {1821cs18, 1821cs13"
}
},
"email": ""
},
{
"first": "Deeksha",
"middle": [],
"last": "Varshney",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Indian Institute of Technology",
"location": {
"addrLine": "Patna {1821cs18, 1821cs13"
}
},
"email": ""
},
{
"first": "Asif",
"middle": [],
"last": "Ekbal",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Indian Institute of Technology",
"location": {
"addrLine": "Patna {1821cs18, 1821cs13"
}
},
"email": ""
},
{
"first": "Pushpak",
"middle": [],
"last": "Bhattacharyya",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Indian Institute of Technology",
"location": {
"addrLine": "Patna {1821cs18, 1821cs13"
}
},
"email": ""
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "Automatic extraction of disaster-related events and their arguments from natural language text is vital for building a decision support system for crisis management. Event extraction from various news sources is a well-explored area for this objective. However, extracting events alone, without any context provides only partial help for this purpose. Extracting related arguments like Time, Place, Casualties, etc., provides a complete picture of the disaster event. In this paper, we create a disaster domain dataset in Hindi by annotating disasterrelated event and arguments. We also obtain equivalent datasets for Bengali and English from a collaboration. We build a multilingual deep learning model for argument extraction in all the three languages. We also compare our multilingual system with a similar baseline monolingual system trained for each language separately. It is observed that a single multilingual system is able to compensate for lack of training data, by using joint training of dataset from different languages in shared space, thus giving a better overall result.",
"pdf_parse": {
"paper_id": "2019",
"_pdf_hash": "",
"abstract": [
{
"text": "Automatic extraction of disaster-related events and their arguments from natural language text is vital for building a decision support system for crisis management. Event extraction from various news sources is a well-explored area for this objective. However, extracting events alone, without any context provides only partial help for this purpose. Extracting related arguments like Time, Place, Casualties, etc., provides a complete picture of the disaster event. In this paper, we create a disaster domain dataset in Hindi by annotating disasterrelated event and arguments. We also obtain equivalent datasets for Bengali and English from a collaboration. We build a multilingual deep learning model for argument extraction in all the three languages. We also compare our multilingual system with a similar baseline monolingual system trained for each language separately. It is observed that a single multilingual system is able to compensate for lack of training data, by using joint training of dataset from different languages in shared space, thus giving a better overall result.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "The ability to extract real time news of disaster events automatically, can potentially help in better decision-making for planning and coordination of disaster relief efforts. Event extraction from text entails the extraction of particular types of events along with their arguments. Information obtained from extracted event mentions provides a more structured and clear picture when augmented with related arguments like Time, Place, Participant, Casualty etc. In a language rich world where each event is documented in multiple languages, argument extraction in multi-lingual setting stands as a crucial task.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Extraction of events from news is a well explored area in Natural Language Processing. Com-petitions such as ACE2005 (Doddington et al., 2004) and TAC-KBP2015 (Mitamura et al., 2015) have investigated the area and provided a large body of literature on event extraction from news articles. Event extraction was done on ACE2005 dataset by Ji and Grishman (2008) by combining global evidence from related documents with local decisions. Hou et al. (2012) introduced a method of event argument extraction based on CRFs model for ACE 2005 Chinese event corpus. Event and its arguments were extracted by Petroni et al. (2018) , for the purpose of extracting breaking news. Although extraction of events is quite well examined, there is a scarcity of work in extraction of detailed arguments for disaster domain like casualties, reason, after-effects etc. In this paper we create and publish a dataset annotated for events in disaster domain, for three different languages, i). Hindi, ii). Bengali and iii). English. This dataset is annotated for the task of argument extraction by expert annotators. We build a 'mono-lingual' deep learning system, based on CNN (Convolutional Neural Network) and Bi-LSTM (Bi-Directional Long Short Term Memory) for the task of argument extraction. In order to leverage the information from all the languages while training, and improve the performance of the system, we build a 'multi-lingual' argument extraction system. This is done by adding separate language layers for each language to our 'mono-lingual' system. To bring the datasets of all the languages to the same vector space, we make use of 'multi-lingual' word embeddings. We show that by training our model in this way we are able to utilize the dataset of all the three languages and improve the performance of our system for most arguments in the three languages. We also investigate how the syntactic difference of the languages is handled by our system. Through analysis, we show that 'multi-lingual' training is espe-cially helpful in improving the performance when some argument is under-represented in the 'monolingual' training data.",
"cite_spans": [
{
"start": 117,
"end": 142,
"text": "(Doddington et al., 2004)",
"ref_id": "BIBREF6"
},
{
"start": 159,
"end": 182,
"text": "(Mitamura et al., 2015)",
"ref_id": "BIBREF19"
},
{
"start": 338,
"end": 360,
"text": "Ji and Grishman (2008)",
"ref_id": "BIBREF12"
},
{
"start": 435,
"end": 452,
"text": "Hou et al. (2012)",
"ref_id": "BIBREF10"
},
{
"start": 599,
"end": 620,
"text": "Petroni et al. (2018)",
"ref_id": "BIBREF26"
},
{
"start": 823,
"end": 849,
"text": "reason, after-effects etc.",
"ref_id": null
},
{
"start": 1152,
"end": 1186,
"text": "CNN (Convolutional Neural Network)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Argument extraction entails classifying each word in the sentence into some argument or not argument. Therefore, it has been formulated as a sequence labelling task. Given a sentence of form w 1 , w 2 , ..., w n , the task is to predict the sequence of event-arguments, of the form l 1 , l 2 , ..., l n . Six different types of arguments were annotated in the dataset: i). Place, ii). Time, iii). Reason, iv). Casualties, v). Participant and vi). After-effects. To label multi-word event-arguments, IOB-style encoding is used where B, I and O denote the beginning, intermediate and outside token of an event.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Problem Definition",
"sec_num": "1.1"
},
{
"text": "\u2022 Input Hindi Sentence: \u0917\u0943 \u0939 \u092e\u0902 \u093e\u0932\u092f \u092e\u0941 \u0902 \u092c\u0908 \u0915\u0947 \u092c\u092e \u0935\u094d \u092b\u094b\u091f \u0915\u0947 \u092e \u0947 \u0928\u091c\u0930 \u0907\u0938 \u092c\u093e\u0924 \u0915 \u0935\u0936\u0947 \u0937 \u0924\u094c\u0930 \u092a\u0930 \u091c\u093e\u0902 \u091a \u0915\u0930 \u0930\u0939\u093e \u0939\u0948 \u0915 \u0905 \u0930\u0927\u093e\u092e \u092e\u0902 \u0926\u0930 \u0914\u0930 \u0967\u096f\u096f\u0969 \u0915\u0947 \u092e\u0941 \u0902 \u092c\u0908 \u092c\u092e \u0935-\u092b\u094b\u091f \u0915\u0947 \u092b\u0938\u0932 \u0915 \u0924 \u092f\u093e \u0915\u0947 \u092a \u092e \u0924\u094b \u092f\u0939 \u0939\u092e\u0932\u0947 \u0928\u0939 \u090f",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Problem Definition",
"sec_num": "1.1"
},
{
"text": "\u2022 Translation: In view of the Mumbai bomb blasts, the Home Ministry is specially investigating the fact that these attacks did not take place as response to the Akshardham Temple and the 1993 Bombay bomb blasts",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Problem Definition",
"sec_num": "1.1"
},
{
"text": "\u2022 Output: O O I_Place O O O O O O O O O O O O O O O O I_Place I_Place O I_Time O I_Place O O O O O O O O O O O O O O",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Problem Definition",
"sec_num": "1.1"
},
{
"text": "A major task in information extraction is detection of event triggers, event classification and event argument extraction. Recent works on event trigger detection and classification discuss efficient feature representation techniques which can help in event extraction. Nguyen and Grishman (2015) proposed a convolutional neural network for event extraction which automatically learns features from text. Chen et al. (2015) Previously, in event argument extraction researchers have experimented with pattern based methods (Patwardhan and Riloff, 2007; Chambers and Jurafsky, 2011) and machine learning based methods (Patwardhan and Riloff, 2009; Lu and Roth, 2012) most of which utilise the various kinds of features obtained from the context of a sentence. Higher level representations such as crosssentence or cross-event information were also explored by Hong et al. (2011) and Huang and Riloff (2011) . Maximum Entropy based classifiers were applied for event and argument labeling by Ahn (2006) ; Chen and Ji (2009) ; Zhao et al. (2008) . The disadvantage with ME classifier is that it gets stuck in local optima and fails to fully capture the context features. To overcome this Hou et al. 2012proposes a event argument extraction system based on Conditional Random Fields (CRF) model that can select any features and normalizing these features in overall situation helps in obtaining optimal results. While, these models can get affected by the error propagated from upstream tasks, a joint model can help us utilise the close interaction between one or more similar tasks. Li et al. (2013) presented a joint model for Chinese Corpus which identifies arguments and determines their roles for event extraction using various kinds of discourse-level information. On ACE2005 dataset Sha et al. (2018) proposed a dependency bridge recurrent neural network (dbRNN) built upon LSTM units for event extraction. They use dependency bridges over Bi-LSTM to join syntactically similar words. A tensor layer is applied to get the various argument-argument interactions. Event triggers and arguments are then jointly extracted utilising a max-margin criterion. Nguyen et al. (2016) presented a GRU model to jointly predict events and its arguments.",
"cite_spans": [
{
"start": 405,
"end": 423,
"text": "Chen et al. (2015)",
"ref_id": "BIBREF3"
},
{
"start": 522,
"end": 551,
"text": "(Patwardhan and Riloff, 2007;",
"ref_id": "BIBREF24"
},
{
"start": 552,
"end": 580,
"text": "Chambers and Jurafsky, 2011)",
"ref_id": "BIBREF2"
},
{
"start": 616,
"end": 645,
"text": "(Patwardhan and Riloff, 2009;",
"ref_id": "BIBREF25"
},
{
"start": 646,
"end": 664,
"text": "Lu and Roth, 2012)",
"ref_id": "BIBREF18"
},
{
"start": 858,
"end": 876,
"text": "Hong et al. (2011)",
"ref_id": "BIBREF9"
},
{
"start": 881,
"end": 904,
"text": "Huang and Riloff (2011)",
"ref_id": "BIBREF11"
},
{
"start": 989,
"end": 999,
"text": "Ahn (2006)",
"ref_id": "BIBREF0"
},
{
"start": 1002,
"end": 1020,
"text": "Chen and Ji (2009)",
"ref_id": "BIBREF4"
},
{
"start": 1023,
"end": 1041,
"text": "Zhao et al. (2008)",
"ref_id": "BIBREF29"
},
{
"start": 1580,
"end": 1596,
"text": "Li et al. (2013)",
"ref_id": "BIBREF15"
},
{
"start": 1786,
"end": 1803,
"text": "Sha et al. (2018)",
"ref_id": "BIBREF28"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Works",
"sec_num": "2"
},
{
"text": "We introduce two systems for the task of event argument extraction. First is our monolingual system built using CNN (Convolutional Neural Network) and Bi-LSTM (Bi-Directional Long Short Term Memory). To exploit the information from related languages, we develop a second system that can use information from all the languages for training. This multi-lingual system is built by using shared vector space of embeddings while training, and by using separate language layers for each language to accommodate for diversity in syntax of the languages.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Related Works",
"sec_num": "2"
},
{
"text": "In this paper, we propose that joint training of IE system on different language datasets, using 'multi-lingual' word embeddings and language layers helps in better extraction of arguments. This is particularly true when the dataset is limited in size. To corroborate our claim, we device two different systems, i). monolingual baseline system, and ii). multi-lingual system. The 'monolingual baseline' system only takes input data (sentence wise) from one language and extracts the arguments. For word representation, it uses monolingual word embeddings. The 'multi-lingual' argument extraction system uses separate language layers and multi-lingual word embeddings for joint training on all the three languages.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Methodology",
"sec_num": "3"
},
{
"text": "The monolingual word-embeddings that are used in our experiments are also known as fastText 1 . It was proposed by Bojanowski et al. (2017) , and is based on the skipgram model. However instead of using one-hot vector encoding for each word while training, a vector representation of a word that considers character n-grams occurring in the word is formed. To get this representation, the n-grams from all the words for 'n' greater than 2 and smaller than 7 are extracted. After this, a dictionary of all the extracted n-grams is created. A given word w, can now be denoted by \u0393 w \u2282 {1, ...., G} i.e the set of n-grams appearing in the word; where G is the size of the n-gram dictionary. With each n-gram in G, a vector representation z g is associated. A word represention is obtained by summing up all the n-grams, as described in Equation 1:",
"cite_spans": [
{
"start": 115,
"end": 139,
"text": "Bojanowski et al. (2017)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Monolingual Word Embedding",
"sec_num": "3.0.1"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "V w = \u2211 g\u2208Gw z g",
"eq_num": "(1)"
}
],
"section": "Monolingual Word Embedding",
"sec_num": "3.0.1"
},
{
"text": "The continuous skip-gram model used these word vectors V w , to obtain word-embedding representa-tions of words. The main advantage of this technique is that, even in the absence of some word in the training corpus, some representations of the word is still obtained as the n-gram representation of words is considered. This skip-gram model is trained using Wikipedia data dump of each language. The dimension of the word vector to is set to 300.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Monolingual Word Embedding",
"sec_num": "3.0.1"
},
{
"text": "Multi-lingual embeddings are obtained by learning a mapping matrix W , between source embeddings X = {x 1 , x 2 , x 3 ..., x n } and target embeddings Y = {y 1 , y 2 , y 3 , ..., y n } without crosslingual supervision.Adversarial training was used in this method proposed by Conneau et al. (2017) . A discriminator is trained to discriminate between a randomly sampled element from W X = {W x 1 , ..., W x n } and Y . At the same time W is trained to prevent the discriminator from making correct prediction. Thus making it a two-player game, where the discriminator tries to maximize its capability of identifying the origins of an embedding, and W aims to prevent the discriminator from doing so by making W X and Y as indistinguishable as possible. The W matrix is trained with near orthogonality constraint, to ensure that while transforming the source vector to the target vector space, the angles and distances between words in the embeddings are not distorted during transformation. To achieve this near orthogonality constraint, weight updation for W is done using Equation 2.",
"cite_spans": [
{
"start": 275,
"end": 296,
"text": "Conneau et al. (2017)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Multi-lingual Word Embedding",
"sec_num": "3.0.2"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "W \u2190 (1 + \u03b2)W \u2212 \u03b2(W W T )W",
"eq_num": "(2)"
}
],
"section": "Multi-lingual Word Embedding",
"sec_num": "3.0.2"
},
{
"text": "Here, \u03b2 was set to 0.01 for the transformation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Multi-lingual Word Embedding",
"sec_num": "3.0.2"
},
{
"text": "For our experiments we trained mapping matrices W hindi and W bengali that map the Hindi and Bengali word embeddings to the vector space of English embeddings.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Multi-lingual Word Embedding",
"sec_num": "3.0.2"
},
{
"text": "The 'monolingual baseline' model (c.f Figure 1 ) is based on Bi-Directional Long Short Term Memory (Bi-LSTM) (Hochreiter and Schmidhuber, 1997; Schuster and Paliwal, 1997) and Convolutional Neural Networks (CNN) (Kim, 2014) . The input to the model is a sentence, represented by a sequence of monolingual word embeddings. Since Bi-LSTM and CNN take sequences of equal lengths, the shorter sequences are padded by zero vectors. This sequence is passed through Bi-LSTM and CNN having filter size 2 and 3. The Bi-LSTM gives contextual representation of each word, while the CNN extracts the 'bi-gram' and 'tri-gram' features for the sequence. These features are concatenated and passed through a fully connected layer. This layer gives shared representation for the task of argument extraction. Since the arguments in the dataset are not mutually exclusive (E.g: Place or Participant argument can also be a part of Reason or After-effect argument), we have different layers to predict different arguments independently. We have 6 different fullyconnected layers in parallel, each of them specialized for detection of one of the 6 arguments. 'Softmax' is used after each of the final layers to classify the representation into I, O or B of an argument.",
"cite_spans": [
{
"start": 109,
"end": 143,
"text": "(Hochreiter and Schmidhuber, 1997;",
"ref_id": "BIBREF8"
},
{
"start": 144,
"end": 171,
"text": "Schuster and Paliwal, 1997)",
"ref_id": "BIBREF27"
},
{
"start": 212,
"end": 223,
"text": "(Kim, 2014)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [
{
"start": 38,
"end": 46,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Monolingual Baseline Model",
"sec_num": "3.1"
},
{
"text": "For multi-lingual system, we build a model based on the baseline model, by adding separate language layers (L 1 , L 2 and L 3 ) for each language (c.f Figure 2) . A layer L i and its subsequent layers are only trained when input data is also of language L i . We represent the input sentence as a sequence of multi-lingual word embeddings, and padding with zero vectors is used to make the sequence equal in length. Similar to the 'monolingual baseline' model, Bi-LSTM, CNN and a fully connected layer is used. This fully connected layer ",
"cite_spans": [],
"ref_spans": [
{
"start": 151,
"end": 160,
"text": "Figure 2)",
"ref_id": "FIGREF1"
}
],
"eq_spans": [],
"section": "Multi-lingual Model",
"sec_num": "3.2"
},
{
"text": "In this section, we describe the dataset used and the experiments conducted.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dataset and Experiments",
"sec_num": "4"
},
{
"text": "To create the dataset, we crawled news articles in disaster domain from popular news websites in Hindi. These news articles were annotated by three annotators, with good language abilities and having satisfactory knowledge in the relevant area. The guidelines for annotation used were similar to the guidelines given by TAC KBP 2017 Event Sequence Annotation Guidelines 2 . We recorded that the annotators had Kappa agreement score of 0.85 Time 3,953 11,042 822 Place 12,410 10,576 3,018 Reason 1,573 1,744 544 Casualties 12,171 15,870 4,823 Participant 2,264 4,311 639 After-effects 13,355 9,731 274 ",
"cite_spans": [],
"ref_spans": [
{
"start": 440,
"end": 608,
"text": "Time 3,953 11,042 822 Place 12,410 10,576 3,018 Reason 1,573 1,744 544 Casualties 12,171 15,870 4,823 Participant 2,264 4,311 639 After-effects 13,355",
"ref_id": "TABREF1"
}
],
"eq_spans": [],
"section": "Dataset",
"sec_num": "4.1"
},
{
"text": "We conduct two separate experiments to show that dataset from different languages (L 1 and L 2 ) can be leveraged to improve the performance of argument extraction system of a different language (L 3 ). First we conduct experiment to obtain baseline results on 'mono-lingual' setup. Next, we perform experiment using the combined dataset of all the three languages using 'multi-lingual' argument extraction model.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments",
"sec_num": "4.2"
},
{
"text": "This experiment is conducted separately on each dataset using the 'monolingual baseline model' (c.f. Figure 1 ) and monolingual fastText embeddings. The results of this experiment is used as a baseline, against which the results of the other experiment is compared. The following set-up is used for the experiment: i). learning rate: 1 \u00d7 10 \u22122 , ii). batch size: 32, iii). optimizer: Adam (Kingma and Ba, 2014), iv). loss function: Binary cross-entropy. The best model based on validation-set accuracy was saved after 100 epochs.",
"cite_spans": [],
"ref_spans": [
{
"start": 101,
"end": 109,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Monolingual Experiment",
"sec_num": "4.2.1"
},
{
"text": "This experiment is conducted on the combined dataset of three languages, using the 'multi-lingual model' (c.f Figure 2 ). Multi-lingual word embeddings (described in Section 3.2) were used for word representation in all the three languages, in this experiment. The same experimental set-up used for the 'monolingual baseline' experiment, is also used for this experiment. The training of multilingual system was done batch wise, i.e. each language branch was trained for one batch alternatively. The number of steps per epochs was decided by the number of batches needed to complete one epoch of the largest training set, among the different language datasets.",
"cite_spans": [],
"ref_spans": [
{
"start": 110,
"end": 118,
"text": "Figure 2",
"ref_id": "FIGREF1"
}
],
"eq_spans": [],
"section": "Multi-lingual Experiment",
"sec_num": "4.2.2"
},
{
"text": "In this section, we discuss the results obtained for the two experiments described in Section 4.2. We also provide analysis of the results. F1-Score is used as an evaluation metric, and all the results reported are 5-Fold cross-validated. The results for both, 'monolingual' and 'cross-lingual' experiments are reported in Table 2 . From the results, it can be observed that F1-score for Hindi and English datasets improve for most arguments (5 out of 6 arguments), while the results for Bengali dataset improves for three out of the six arguments.",
"cite_spans": [],
"ref_spans": [
{
"start": 323,
"end": 330,
"text": "Table 2",
"ref_id": "TABREF3"
}
],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "We also test the statistical significance of each increment in F1-Score for argument extraction. The 'p-values' obtained after 't-test' are shown in Table 3 . It can be seen that most improvements in F1-score are statistically significant.",
"cite_spans": [],
"ref_spans": [
{
"start": 149,
"end": 156,
"text": "Table 3",
"ref_id": "TABREF4"
}
],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "It is observed that multi-word Time arguments are better captured by 'multi-lingual' model than by the 'monolingual baseline' model. An example of this can be seen in the following sentence:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "\u2022 Hindi Text: \u090f\u0938\u090f\u0938\u092a\u0940 \u0938\u0902 \u0924\u094b\u0937 \u0915\u0941 \u092e\u093e\u0930 \u0938 \u0939 \u0928\u0947 \u092c\u0924\u093e\u092f\u093e \u0915 \u0930 \u0935\u0935\u093e\u0930 \u0930\u093e\u0924 \u0915\u094b \u091c\u0932\u093e\u0932\u092a\u0941 \u0930 \u092a\u0930 \u0924\u0948 \u0928\u093e\u0924 \u092a\u0941 \u0932\u0938\u0915 \u092e \u092f \u0928\u0947 \u092c\u093e\u0907\u0915 \u092a\u0930 \u0938\u0935\u093e\u0930 \u0926\u094b \u092f\u0941 \u0935\u0915 \u0915\u094b \u0930\u094b\u0915\u0928\u0947 \u0915 \u0915\u094b \u0936\u0936 \u0915",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "\u2022 Transliteration: esesapee santosh kumaar sinh ne bataaya ki ravivaar raat ko jalaalapur par tainaat pulisakarmiyon ne baik par savaar do yuvakon ko rokane kee koshish kee",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "\u2022 Translation: SSP Santosh Kumar Singh said that on Sunday night, policemen stationed at Jalalpur tried to stop two youths riding on bikes.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "In the aforementioned sentence the actual phrase denoting time is '\u0930 \u0935\u0935\u093e\u0930 \u0930\u093e\u0924' (Sunday night). However the 'monolingual' model only detects '\u0930 \u0935\u0935\u093e\u0930' (Sunday) as the Time argument. However, after multi-lingual training the entire time phrase is correctly detected. This is because the lack of training data for multi-word time arguments in Hindi, is supplemented by training data from Bengali and English.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "Another interesting observation is that, for Casualty argument of English dataset, the 'monolingual' system often confuses people as casualties, even when they are not. An example of such observation is as follows:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "\u2022 Actual: Over 200000 people in 36 villages located 6 miles (10 km) from the volcano were advised to evacuate immediately.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "\u2022 Monolingual Prediction: Over 200000 people in 36 villages located 6 miles (10 km) from the volcano were advised to evacuate immediately.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "\u2022 Multi-lingual Prediction: Over 200000 people in 36 villages located 6 miles (10 km) from the volcano were advised to evacuate immediately.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "In the above example the phrase '200000 people' does not denote casualty, however the 'monolingual' model confuses it as casualty. This is due to the lack of training data in English to learn the difference between some count of people and actual casualty. However, after 'multi-lingual' train-ing the model is able to make this distinction correctly.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "The F1-score for Place arguments for all the datasets, is better for the 'monolingual baseline' model. This is because Place argument is present in good numbers for all the datasets, therefore there are enough instances for proper training of deep learning model, even in monolingual setting. Using 'multi-lingual model' for such cases is of little help. Furthermore, the syntactic difference between languages confuses the system, thus degrading the performance of the 'multi-lingual' system. A good example of this phenomenon is show below:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "\u2022 Actual: Three youths lost their lives when the car they were travelling in collided with a truck near Gaddoli village of Naraingarh in Ambala.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "\u2022 Monolingual Prediction: Three youths lost their lives when the car they were travelling in collided with a truck near Gaddoli village of Naraingarh in Ambala.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "\u2022 Multi-lingual Prediction: Three youths lost their lives when the car they were travelling in collided with a truck near Gaddoli village of Naraingarh in Ambala.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "It can be observed that the 'monolingual baseline' model predicts the entire phrase describing the Place argument correctly. However the prediction by 'multi-lingual model' misses the preposition 'in', which is present between 'Naraingarh' and 'Ambala'. The same sentence can be written in Bengali as follows:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "\u2022 Bengali Transliteration: Amb\u0101l\u0101ra n\u0101r\u0101y\u0227naga\u1e5b\u0113ra g\u0101ddali gr\u0101m\u0113ra k\u0101ch\u0113 \u0113ka\u1e6di \u1e6dr\u0101k\u0113ra s\u0101th\u0113 \u1e6dr\u0113n\u0113ra mukh\u014dmukhi sa\u1e45ghar\u1e63\u0113 tinajana yubaka pr\u0101\u1e47a h\u0101r\u0101y\u0227.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "The phrase 'in Ambala' is represented by a single word 'Amb\u0101l\u0101ra', in Bengali. This difference in syntax between languages, makes the 'multilingual' system miss the word 'in' thus degrading the performance of the system.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "The best improvement in F1-score is observed for the arguments Reason and After-effects for the English language. This is because these two arguments have least support in the dataset, and thus multi-lingual training helps by mitigating the scarcity in training examples. The same phenomenon can also be observed for Reason argument which has a low support in Hindi dataset. Thus through our analysis we can conclude that, 'multi-lingual' training can help in improving the performance of the system for low support classes. However, it can also cause confusion and deteriorate the performance for high support classes.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Analysis",
"sec_num": "5"
},
{
"text": "In this paper we create a dataset for argument extraction for disaster domain, for three languages Hindi, Bengali and English. We then build a deep learning model for extraction of these argument in each language separately. Since the data is limited in size, we build another model that leverages data from all the languages. To make use of different language datasets, we first bring the word embeddings of all the three languages to the same vector space. We also use separate language layers to accommodate divergence in syntax of the languages. Through our experiments we show that training in shared vector space by using 'multi-lingual' system helps in improving the performance of low support arguments. We also show that the for high support arguments, the syntactic difference in language can sometimes overcome the benefit of 'multi-lingual' training and cost in performance of our proposed 'multi-lingual' system.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "6"
},
{
"text": "In future we would like to explore how to handle these syntactic differences so that the performance can be further improved. It would also be interesting to explore the range of languages that can be trained successfully in a multi-lingual setting.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "6"
},
{
"text": "https://github.com/facebookresearch/ fastText",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "https://cairo.lti.cs.cmu.edu/kbp/2017/ event/TAC_KBP_2017_Event_Coreference_and_ Sequence_Annotation_Guidelines_v1.1.pdf",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "The research reported in this paper is an outcome of the project titled \"A Platform for Cross-lingual and Multi-lingual Event Monitoring in Indian Languages\", supported by IMPRINT-1, MHRD, Govt. of India, and MeiTY, Govt. of India.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgement",
"sec_num": "7"
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "The stages of event extraction",
"authors": [
{
"first": "David",
"middle": [],
"last": "Ahn",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of the Workshop on Annotating and Reasoning about Time and Events",
"volume": "",
"issue": "",
"pages": "1--8",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "David Ahn. 2006. The stages of event extraction. In Proceedings of the Workshop on Annotating and Reasoning about Time and Events, pages 1-8.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Enriching word vectors with subword information",
"authors": [
{
"first": "Piotr",
"middle": [],
"last": "Bojanowski",
"suffix": ""
},
{
"first": "Edouard",
"middle": [],
"last": "Grave",
"suffix": ""
},
{
"first": "Armand",
"middle": [],
"last": "Joulin",
"suffix": ""
},
{
"first": "Tomas",
"middle": [],
"last": "Mikolov",
"suffix": ""
}
],
"year": 2017,
"venue": "Transactions of the Association for Computational Linguistics",
"volume": "5",
"issue": "",
"pages": "135--146",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching word vectors with subword information. Transactions of the Associa- tion for Computational Linguistics, 5:135-146.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Template-based information extraction without the templates",
"authors": [
{
"first": "Nathanael",
"middle": [],
"last": "Chambers",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Jurafsky",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
"volume": "1",
"issue": "",
"pages": "976--986",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Nathanael Chambers and Dan Jurafsky. 2011. Template-based information extraction without the templates. In Proceedings of the 49th Annual Meeting of the Association for Computational Lin- guistics: Human Language Technologies-Volume 1, pages 976-986. Association for Computational Linguistics.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Event extraction via dynamic multipooling convolutional neural networks",
"authors": [
{
"first": "Yubo",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Liheng",
"middle": [],
"last": "Xu",
"suffix": ""
},
{
"first": "Kang",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Daojian",
"middle": [],
"last": "Zeng",
"suffix": ""
},
{
"first": "Jun",
"middle": [],
"last": "Zhao",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing",
"volume": "1",
"issue": "",
"pages": "167--176",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yubo Chen, Liheng Xu, Kang Liu, Daojian Zeng, and Jun Zhao. 2015. Event extraction via dynamic multi- pooling convolutional neural networks. In Proceed- ings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th Interna- tional Joint Conference on Natural Language Pro- cessing (Volume 1: Long Papers), pages 167-176.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Language specific issue and feature exploration in chinese event extraction",
"authors": [
{
"first": "Zheng",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Heng",
"middle": [],
"last": "Ji",
"suffix": ""
}
],
"year": 2009,
"venue": "The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers",
"volume": "",
"issue": "",
"pages": "209--212",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zheng Chen and Heng Ji. 2009. Language specific issue and feature exploration in chinese event ex- traction. In Proceedings of Human Language Tech- nologies: The 2009 Annual Conference of the North American Chapter of the Association for Computa- tional Linguistics, Companion Volume: Short Pa- pers, pages 209-212.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Word translation without parallel data",
"authors": [
{
"first": "Alexis",
"middle": [],
"last": "Conneau",
"suffix": ""
},
{
"first": "Guillaume",
"middle": [],
"last": "Lample",
"suffix": ""
},
{
"first": "Marc'aurelio",
"middle": [],
"last": "Ranzato",
"suffix": ""
},
{
"first": "Ludovic",
"middle": [],
"last": "Denoyer",
"suffix": ""
},
{
"first": "Herv\u00e9",
"middle": [],
"last": "J\u00e9gou",
"suffix": ""
}
],
"year": 2017,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1710.04087"
]
},
"num": null,
"urls": [],
"raw_text": "Alexis Conneau, Guillaume Lample, Marc'Aurelio Ranzato, Ludovic Denoyer, and Herv\u00e9 J\u00e9gou. 2017. Word translation without parallel data. arXiv preprint arXiv:1710.04087.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "The automatic content extraction (ace) program-tasks, data, and evaluation",
"authors": [
{
"first": "Alexis",
"middle": [],
"last": "George R Doddington",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Mitchell",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Mark",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Przybocki",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Lance",
"suffix": ""
},
{
"first": "Stephanie",
"middle": [
"M"
],
"last": "Ramshaw",
"suffix": ""
},
{
"first": "Ralph",
"middle": [
"M"
],
"last": "Strassel",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Weischedel",
"suffix": ""
}
],
"year": 2004,
"venue": "LREC",
"volume": "2",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "George R Doddington, Alexis Mitchell, Mark A Przy- bocki, Lance A Ramshaw, Stephanie M Strassel, and Ralph M Weischedel. 2004. The automatic content extraction (ace) program-tasks, data, and evaluation. In LREC, volume 2, page 1.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "A language-independent neural network for event detection",
"authors": [
{
"first": "Xiaocheng",
"middle": [],
"last": "Feng",
"suffix": ""
},
{
"first": "Bing",
"middle": [],
"last": "Qin",
"suffix": ""
},
{
"first": "Ting",
"middle": [],
"last": "Liu",
"suffix": ""
}
],
"year": 2018,
"venue": "Science China Information Sciences",
"volume": "61",
"issue": "9",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xiaocheng Feng, Bing Qin, and Ting Liu. 2018. A language-independent neural network for event detection. Science China Information Sciences, 61(9):092106.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Long short-term memory",
"authors": [
{
"first": "Sepp",
"middle": [],
"last": "Hochreiter",
"suffix": ""
},
{
"first": "J\u00fcrgen",
"middle": [],
"last": "Schmidhuber",
"suffix": ""
}
],
"year": 1997,
"venue": "Neural computation",
"volume": "9",
"issue": "8",
"pages": "1735--1780",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735-1780.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Using cross-entity inference to improve event extraction",
"authors": [
{
"first": "Yu",
"middle": [],
"last": "Hong",
"suffix": ""
},
{
"first": "Jianfeng",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Bin",
"middle": [],
"last": "Ma",
"suffix": ""
},
{
"first": "Jianmin",
"middle": [],
"last": "Yao",
"suffix": ""
},
{
"first": "Guodong",
"middle": [],
"last": "Zhou",
"suffix": ""
},
{
"first": "Qiaoming",
"middle": [],
"last": "Zhu",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
"volume": "1",
"issue": "",
"pages": "1127--1136",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yu Hong, Jianfeng Zhang, Bin Ma, Jianmin Yao, Guodong Zhou, and Qiaoming Zhu. 2011. Us- ing cross-entity inference to improve event extrac- tion. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Hu- man Language Technologies-Volume 1, pages 1127- 1136. Association for Computational Linguistics.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Event argument extraction based on crf",
"authors": [
{
"first": "Libin",
"middle": [],
"last": "Hou",
"suffix": ""
},
{
"first": "Peifeng",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Qiaoming",
"middle": [],
"last": "Zhu",
"suffix": ""
},
{
"first": "Yuan",
"middle": [],
"last": "Cao",
"suffix": ""
}
],
"year": 2012,
"venue": "Workshop on Chinese Lexical Semantics",
"volume": "",
"issue": "",
"pages": "32--39",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Libin Hou, Peifeng Li, Qiaoming Zhu, and Yuan Cao. 2012. Event argument extraction based on crf. In Workshop on Chinese Lexical Semantics, pages 32- 39. Springer.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Peeling back the layers: detecting event role fillers in secondary contexts",
"authors": [
{
"first": "Ruihong",
"middle": [],
"last": "Huang",
"suffix": ""
},
{
"first": "Ellen",
"middle": [],
"last": "Riloff",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
"volume": "1",
"issue": "",
"pages": "1137--1147",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ruihong Huang and Ellen Riloff. 2011. Peeling back the layers: detecting event role fillers in secondary contexts. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pages 1137-1147. Association for Computational Linguis- tics.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Refining event extraction through cross-document inference",
"authors": [
{
"first": "Heng",
"middle": [],
"last": "Ji",
"suffix": ""
},
{
"first": "Ralph",
"middle": [],
"last": "Grishman",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of ACL-08: HLT",
"volume": "",
"issue": "",
"pages": "254--262",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Heng Ji and Ralph Grishman. 2008. Refining event extraction through cross-document inference. Pro- ceedings of ACL-08: HLT, pages 254-262.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Convolutional neural networks for sentence classification",
"authors": [
{
"first": "Yoon",
"middle": [],
"last": "Kim",
"suffix": ""
}
],
"year": 2014,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1408.5882"
]
},
"num": null,
"urls": [],
"raw_text": "Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Adam: A method for stochastic optimization",
"authors": [
{
"first": "P",
"middle": [],
"last": "Diederik",
"suffix": ""
},
{
"first": "Jimmy",
"middle": [],
"last": "Kingma",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Ba",
"suffix": ""
}
],
"year": 2014,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1412.6980"
]
},
"num": null,
"urls": [],
"raw_text": "Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Joint modeling of argument identification and role determination in chinese event extraction with discourse-level information",
"authors": [
{
"first": "Peifeng",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Qiaoming",
"middle": [],
"last": "Zhu",
"suffix": ""
},
{
"first": "Guodong",
"middle": [],
"last": "Zhou",
"suffix": ""
}
],
"year": 2013,
"venue": "Twenty-Third International Joint Conference on Artificial Intelligence",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Peifeng Li, Qiaoming Zhu, and Guodong Zhou. 2013. Joint modeling of argument identification and role determination in chinese event extraction with discourse-level information. In Twenty-Third Inter- national Joint Conference on Artificial Intelligence.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "A multi-lingual multi-task architecture for low-resource sequence labeling",
"authors": [
{
"first": "Ying",
"middle": [],
"last": "Lin",
"suffix": ""
},
{
"first": "Shengqi",
"middle": [],
"last": "Yang",
"suffix": ""
},
{
"first": "Veselin",
"middle": [],
"last": "Stoyanov",
"suffix": ""
},
{
"first": "Heng",
"middle": [],
"last": "Ji",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "799--809",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ying Lin, Shengqi Yang, Veselin Stoyanov, and Heng Ji. 2018. A multi-lingual multi-task architecture for low-resource sequence labeling. In Proceed- ings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pa- pers), pages 799-809.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Event detection via gated multilingual attention mechanism",
"authors": [
{
"first": "Jian",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Yubo",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Kang",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Jun",
"middle": [],
"last": "Zhao",
"suffix": ""
}
],
"year": 2018,
"venue": "Thirty-Second AAAI Conference on Artificial Intelligence",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jian Liu, Yubo Chen, Kang Liu, and Jun Zhao. 2018. Event detection via gated multilingual attention mechanism. In Thirty-Second AAAI Conference on Artificial Intelligence.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Automatic event extraction with structured preference modeling",
"authors": [
{
"first": "Wei",
"middle": [],
"last": "Lu",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Roth",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers",
"volume": "1",
"issue": "",
"pages": "835--844",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Wei Lu and Dan Roth. 2012. Automatic event extrac- tion with structured preference modeling. In Pro- ceedings of the 50th Annual Meeting of the Associ- ation for Computational Linguistics: Long Papers- Volume 1, pages 835-844. Association for Compu- tational Linguistics.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Overview of tac kbp 2015 event nugget track",
"authors": [
{
"first": "Teruko",
"middle": [],
"last": "Mitamura",
"suffix": ""
},
{
"first": "Zhengzhong",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Eduard",
"middle": [
"H"
],
"last": "Hovy",
"suffix": ""
}
],
"year": 2015,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Teruko Mitamura, Zhengzhong Liu, and Eduard H Hovy. 2015. Overview of tac kbp 2015 event nugget track. In TAC.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Joint event extraction via recurrent neural networks",
"authors": [
{
"first": "Kyunghyun",
"middle": [],
"last": "Thien Huu Nguyen",
"suffix": ""
},
{
"first": "Ralph",
"middle": [],
"last": "Cho",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Grishman",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "",
"issue": "",
"pages": "300--309",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Thien Huu Nguyen, Kyunghyun Cho, and Ralph Grish- man. 2016. Joint event extraction via recurrent neu- ral networks. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, pages 300-309.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Event detection and domain adaptation with convolutional neural networks",
"authors": [
{
"first": "Huu",
"middle": [],
"last": "Thien",
"suffix": ""
},
{
"first": "Ralph",
"middle": [],
"last": "Nguyen",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Grishman",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing",
"volume": "2",
"issue": "",
"pages": "365--371",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Thien Huu Nguyen and Ralph Grishman. 2015. Event detection and domain adaptation with convolutional neural networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Lin- guistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 365-371.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Modeling skip-grams for event detection with convolutional neural networks",
"authors": [
{
"first": "Huu",
"middle": [],
"last": "Thien",
"suffix": ""
},
{
"first": "Ralph",
"middle": [],
"last": "Nguyen",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Grishman",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "886--891",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Thien Huu Nguyen and Ralph Grishman. 2016. Mod- eling skip-grams for event detection with convolu- tional neural networks. In Proceedings of the 2016 Conference on Empirical Methods in Natural Lan- guage Processing, pages 886-891.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Graph convolutional networks with argument-aware pooling for event detection",
"authors": [
{
"first": "Huu",
"middle": [],
"last": "Thien",
"suffix": ""
},
{
"first": "Ralph",
"middle": [],
"last": "Nguyen",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Grishman",
"suffix": ""
}
],
"year": 2018,
"venue": "Thirty-Second AAAI Conference on Artificial Intelligence",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Thien Huu Nguyen and Ralph Grishman. 2018. Graph convolutional networks with argument-aware pool- ing for event detection. In Thirty-Second AAAI Con- ference on Artificial Intelligence.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Effective information extraction with semantic affinity patterns and relevant regions",
"authors": [
{
"first": "Siddharth",
"middle": [],
"last": "Patwardhan",
"suffix": ""
},
{
"first": "Ellen",
"middle": [],
"last": "Riloff",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)",
"volume": "",
"issue": "",
"pages": "717--727",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Siddharth Patwardhan and Ellen Riloff. 2007. Effective information extraction with semantic affinity pat- terns and relevant regions. In Proceedings of the 2007 Joint Conference on Empirical Methods in Nat- ural Language Processing and Computational Nat- ural Language Learning (EMNLP-CoNLL), pages 717-727.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "A unified model of phrasal and sentential evidence for information extraction",
"authors": [
{
"first": "Siddharth",
"middle": [],
"last": "Patwardhan",
"suffix": ""
},
{
"first": "Ellen",
"middle": [],
"last": "Riloff",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing",
"volume": "1",
"issue": "",
"pages": "151--160",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Siddharth Patwardhan and Ellen Riloff. 2009. A uni- fied model of phrasal and sentential evidence for in- formation extraction. In Proceedings of the 2009 Conference on Empirical Methods in Natural Lan- guage Processing: Volume 1-Volume 1, pages 151- 160. Association for Computational Linguistics.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "An extensible event extraction system with cross-media event resolution",
"authors": [
{
"first": "Fabio",
"middle": [],
"last": "Petroni",
"suffix": ""
},
{
"first": "Natraj",
"middle": [],
"last": "Raman",
"suffix": ""
},
{
"first": "Tim",
"middle": [],
"last": "Nugent",
"suffix": ""
},
{
"first": "Armineh",
"middle": [],
"last": "Nourbakhsh",
"suffix": ""
},
{
"first": "\u017darko",
"middle": [],
"last": "Pani\u0107",
"suffix": ""
},
{
"first": "Sameena",
"middle": [],
"last": "Shah",
"suffix": ""
},
{
"first": "Jochen L",
"middle": [],
"last": "Leidner",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining",
"volume": "",
"issue": "",
"pages": "626--635",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Fabio Petroni, Natraj Raman, Tim Nugent, Armineh Nourbakhsh, \u017darko Pani\u0107, Sameena Shah, and Jochen L Leidner. 2018. An extensible event ex- traction system with cross-media event resolution. In Proceedings of the 24th ACM SIGKDD Interna- tional Conference on Knowledge Discovery & Data Mining, pages 626-635. ACM.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Bidirectional recurrent neural networks",
"authors": [
{
"first": "Mike",
"middle": [],
"last": "Schuster",
"suffix": ""
},
{
"first": "K",
"middle": [],
"last": "Kuldip",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Paliwal",
"suffix": ""
}
],
"year": 1997,
"venue": "IEEE Transactions on Signal Processing",
"volume": "45",
"issue": "11",
"pages": "2673--2681",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mike Schuster and Kuldip K Paliwal. 1997. Bidirec- tional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11):2673-2681.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "Jointly extracting event triggers and arguments by dependency-bridge rnn and tensor-based argument interaction",
"authors": [
{
"first": "Lei",
"middle": [],
"last": "Sha",
"suffix": ""
},
{
"first": "Feng",
"middle": [],
"last": "Qian",
"suffix": ""
},
{
"first": "Baobao",
"middle": [],
"last": "Chang",
"suffix": ""
},
{
"first": "Zhifang",
"middle": [],
"last": "Sui",
"suffix": ""
}
],
"year": 2018,
"venue": "Thirty-Second AAAI Conference on Artificial Intelligence",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Lei Sha, Feng Qian, Baobao Chang, and Zhifang Sui. 2018. Jointly extracting event triggers and argu- ments by dependency-bridge rnn and tensor-based argument interaction. In Thirty-Second AAAI Con- ference on Artificial Intelligence.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "Research on chinese event extraction",
"authors": [
{
"first": "Yan-Yan",
"middle": [],
"last": "Zhao",
"suffix": ""
},
{
"first": "Bing",
"middle": [],
"last": "Qin",
"suffix": ""
},
{
"first": "Che",
"middle": [],
"last": "Wan-Xiang",
"suffix": ""
},
{
"first": "Ting",
"middle": [],
"last": "Liu",
"suffix": ""
}
],
"year": 2008,
"venue": "Journal of Chinese Information Processing",
"volume": "22",
"issue": "1",
"pages": "3--8",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yan-yan Zhao, Bing Qin, Wan-xiang Che, and Ting Liu. 2008. Research on chinese event extraction. Journal of Chinese Information Processing, 22(1):3-8.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"type_str": "figure",
"text": "monolingual baseline model for argument extraction",
"uris": null,
"num": null
},
"FIGREF1": {
"type_str": "figure",
"text": "Multi-lingual baseline model for argument extraction produces shared language and task representation as output. Three separate language layers for the languages Hindi, Bengali and English are used in parallel. These language layers decode the language specific representation from shared representation. After each language layer we have 6 fully connected layers for each of the 6 arguments. 'Softmax' classifier is used to classify the representation into I, O or B of an argument.",
"uris": null,
"num": null
},
"TABREF1": {
"type_str": "table",
"html": null,
"text": "",
"content": "<table><tr><td>: Distribution of number of arguments in Hindi,</td></tr><tr><td>Bengali and English datasets</td></tr><tr><td>on average. We also obtained equivalent dataset</td></tr><tr><td>in Bengali and English language from a collabora-</td></tr><tr><td>tion. The total dataset is comprised of 2,191 doc-</td></tr><tr><td>uments (Hindi: 922, Bengali: 999 and English:</td></tr><tr><td>270). It contains 44,615 sentences (Hindi: 17,116,</td></tr><tr><td>Bengali: 25,717 and English: 1,782). The six ar-</td></tr><tr><td>guments in the dataset and their distribution in the</td></tr><tr><td>three languages are detailed in the Table 1.</td></tr></table>",
"num": null
},
"TABREF3": {
"type_str": "table",
"html": null,
"text": "",
"content": "<table><tr><td colspan=\"4\">: Results (F1-Scores) for 'mono-lingual' and 'multi-lingual' experiments on Hindi, Bengali and English</td></tr><tr><td colspan=\"2\">datasets: 5-Fold cross-validated</td><td/><td/></tr><tr><td>Argument</td><td colspan=\"3\">Hindi Bengali English</td></tr><tr><td>Time</td><td>0.46</td><td>n/a</td><td>0.03</td></tr><tr><td>Place</td><td>n/a</td><td>n/a</td><td>n/a</td></tr><tr><td>Reason</td><td>0.03</td><td>0.18</td><td>0.04</td></tr><tr><td>Casualties</td><td>0.39</td><td>n/a</td><td>0.10</td></tr><tr><td>Participant</td><td>0.01</td><td>0.11</td><td>0.54</td></tr><tr><td colspan=\"2\">After-effects 0.04</td><td>0.09</td><td>0.01</td></tr></table>",
"num": null
},
"TABREF4": {
"type_str": "table",
"html": null,
"text": "",
"content": "<table><tr><td>: The 'p-values' obtained for each improvement</td></tr><tr><td>in results from the baseline 'mono-lingual' to 'multi-</td></tr><tr><td>lingual' experiment (n/a is used for instances where no</td></tr><tr><td>improvement was observed)</td></tr></table>",
"num": null
}
}
}
} |