File size: 88,184 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 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 |
{
"paper_id": "2020",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T07:28:50.706362Z"
},
"title": "Memory Attentive Fusion: External Language Model Integration for Transformer-based Sequence-to-Sequence Model",
"authors": [
{
"first": "Mana",
"middle": [],
"last": "Ihori",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "NTT Corporation",
"location": {
"addrLine": "1-1 Hikarinooka, Yokosuka-Shi",
"postCode": "239-0847",
"settlement": "Kanagawa",
"country": "Japan"
}
},
"email": "mana.ihori.kx@hco.ntt.co.jp"
},
{
"first": "Ryo",
"middle": [],
"last": "Masumura",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "NTT Corporation",
"location": {
"addrLine": "1-1 Hikarinooka, Yokosuka-Shi",
"postCode": "239-0847",
"settlement": "Kanagawa",
"country": "Japan"
}
},
"email": ""
},
{
"first": "Naoki",
"middle": [],
"last": "Makishima",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "NTT Corporation",
"location": {
"addrLine": "1-1 Hikarinooka, Yokosuka-Shi",
"postCode": "239-0847",
"settlement": "Kanagawa",
"country": "Japan"
}
},
"email": ""
},
{
"first": "Tomohiro",
"middle": [],
"last": "Tanaka",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "NTT Corporation",
"location": {
"addrLine": "1-1 Hikarinooka, Yokosuka-Shi",
"postCode": "239-0847",
"settlement": "Kanagawa",
"country": "Japan"
}
},
"email": ""
},
{
"first": "Akihiko",
"middle": [],
"last": "Takashima",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "NTT Corporation",
"location": {
"addrLine": "1-1 Hikarinooka, Yokosuka-Shi",
"postCode": "239-0847",
"settlement": "Kanagawa",
"country": "Japan"
}
},
"email": ""
},
{
"first": "Shota",
"middle": [],
"last": "Orihashi",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "NTT Corporation",
"location": {
"addrLine": "1-1 Hikarinooka, Yokosuka-Shi",
"postCode": "239-0847",
"settlement": "Kanagawa",
"country": "Japan"
}
},
"email": ""
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "This paper presents a novel fusion method for integrating an external language model (LM) into the Transformer based sequenceto-sequence (seq2seq) model. While paired data are basically required to train the seq2seq model, the external LM can be trained with only unpaired data. Thus, it is important to leverage memorized knowledge in the external LM for building the seq2seq model, since it is hard to prepare a large amount of paired data. However, the existing fusion methods assume that the LM is integrated with recurrent neural network-based seq2seq models instead of the Transformer. Therefore, this paper proposes a fusion method that can explicitly utilize network structures in the Transformer. The proposed method, called memory attentive fusion, leverages the Transformer-style attention mechanism that repeats source-target attention in a multi-hop manner for reading the memorized knowledge in the LM. Our experiments on two text-style conversion tasks demonstrate that the proposed method performs better than conventional fusion methods.",
"pdf_parse": {
"paper_id": "2020",
"_pdf_hash": "",
"abstract": [
{
"text": "This paper presents a novel fusion method for integrating an external language model (LM) into the Transformer based sequenceto-sequence (seq2seq) model. While paired data are basically required to train the seq2seq model, the external LM can be trained with only unpaired data. Thus, it is important to leverage memorized knowledge in the external LM for building the seq2seq model, since it is hard to prepare a large amount of paired data. However, the existing fusion methods assume that the LM is integrated with recurrent neural network-based seq2seq models instead of the Transformer. Therefore, this paper proposes a fusion method that can explicitly utilize network structures in the Transformer. The proposed method, called memory attentive fusion, leverages the Transformer-style attention mechanism that repeats source-target attention in a multi-hop manner for reading the memorized knowledge in the LM. Our experiments on two text-style conversion tasks demonstrate that the proposed method performs better than conventional fusion methods.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "In recent studies, the Transformer sequence-tosequence (seq2seq) model (Vaswani et al., 2017) has successfully performed in various natural language generation tasks, such as machine translation Barrault et al., 2019) , image captioning (Li et al., 2019b; Yu et al., 2019; Li et al., 2019a) , and automatic speech recognition (Dong et al., 2018; Karita et al., 2019; Salazar et al., 2019) . Although the Transformer training needs paired data, a large amount of paired data cannot often be prepared. Moreover, unpaired data cannot be used for training the Transformer even though such data can be collected on a large scale.",
"cite_spans": [
{
"start": 71,
"end": 93,
"text": "(Vaswani et al., 2017)",
"ref_id": "BIBREF19"
},
{
"start": 195,
"end": 217,
"text": "Barrault et al., 2019)",
"ref_id": null
},
{
"start": 237,
"end": 255,
"text": "(Li et al., 2019b;",
"ref_id": "BIBREF10"
},
{
"start": 256,
"end": 272,
"text": "Yu et al., 2019;",
"ref_id": "BIBREF21"
},
{
"start": 273,
"end": 290,
"text": "Li et al., 2019a)",
"ref_id": "BIBREF9"
},
{
"start": 326,
"end": 345,
"text": "(Dong et al., 2018;",
"ref_id": "BIBREF4"
},
{
"start": 346,
"end": 366,
"text": "Karita et al., 2019;",
"ref_id": "BIBREF8"
},
{
"start": 367,
"end": 388,
"text": "Salazar et al., 2019)",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "To utilize a large amount of unpaired data, methods of integrating an external language model (LM) trained with these data into seq2seq mod-els have been proposed (Kannan et al., 2018; Gulcehre et al., 2015; Sriram et al., 2018) . These methods can improve the fluency of sentences that are generated by seq2seq models; however, they integrate the LM into recurrent neural network (RNN) based seq2seq models rather than the Transformer. In other words, LM fusion methods specific to the Transformer have not been considered yet.",
"cite_spans": [
{
"start": 163,
"end": 184,
"text": "(Kannan et al., 2018;",
"ref_id": "BIBREF7"
},
{
"start": 185,
"end": 207,
"text": "Gulcehre et al., 2015;",
"ref_id": "BIBREF5"
},
{
"start": 208,
"end": 228,
"text": "Sriram et al., 2018)",
"ref_id": "BIBREF16"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Here, the Transformer employs the multi-hop attention mechanism (Sukhbaatar et al., 2015 ) that repeats the source-target attention mechanism in each Transformer decoder block. Thus, it is supposed that the source-target attention mechanism promotes to extract effective source information for target tasks more exactly than RNN based seq2seq models. Therefore, we assume that the Transformer can utilize memorized knowledge in the external LM more effectively by using the multi-hop attention mechanism for the LM fusion.",
"cite_spans": [
{
"start": 64,
"end": 88,
"text": "(Sukhbaatar et al., 2015",
"ref_id": "BIBREF17"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this paper, we propose a novel fusion method, called memory attentive fusion, to integrate an external LM into the Transformer. This fusion method utilizes a multi-hop source-target attention mechanism for combining the Transformer decoder with the external LM. We performed experiments with two text-style conversion tasks: spoken-to-written style conversion and dialect conversion. Our experiments demonstrate that the proposed method performs better than conventional fusion methods.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The simplest fusion method is to train the seq2seq model and the LM independently and then combine their outputs (Kannan et al., 2018; Chorowski and Jaitly, 2017; Sutskever et al., 2014) . These methods are called shallow fusion.",
"cite_spans": [
{
"start": 113,
"end": 134,
"text": "(Kannan et al., 2018;",
"ref_id": "BIBREF7"
},
{
"start": 135,
"end": 162,
"text": "Chorowski and Jaitly, 2017;",
"ref_id": "BIBREF3"
},
{
"start": 163,
"end": 186,
"text": "Sutskever et al., 2014)",
"ref_id": "BIBREF18"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related work",
"sec_num": "2"
},
{
"text": "Moreover, methods that integrate an external LM into seq2seq models during training have been proposed: deep fusion (Gulcehre et al., 2015) and cold fusion (Sriram et al., 2018) . These methods utilize the information of not only paired data but also unpaired data in training. Figure 1 shows a Transformer with cold fusion. These methods assume that the LM is integrated into RNN-based seq2seq models instead of the Transformer.",
"cite_spans": [
{
"start": 116,
"end": 139,
"text": "(Gulcehre et al., 2015)",
"ref_id": "BIBREF5"
},
{
"start": 156,
"end": 177,
"text": "(Sriram et al., 2018)",
"ref_id": "BIBREF16"
}
],
"ref_spans": [
{
"start": 278,
"end": 286,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Related work",
"sec_num": "2"
},
{
"text": "This section details memory attentive fusion for the Transformer seq2seq model. In fact, memory attentive fusion is an extended method of the cold fusion (Sriram et al., 2018) . While the cold fusion uses memorized knowledge in the LM at an output layer only once, the memory attentive fusion repeatedly uses the knowledge at Transformer decoder blocks based on a source-target attention mechanism.",
"cite_spans": [
{
"start": 154,
"end": 175,
"text": "(Sriram et al., 2018)",
"ref_id": "BIBREF16"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "We define an input sequence as",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "X = {x 1 , \u2022 \u2022 \u2022 , x M } and an output sequence as Y = {y 1 , \u2022 \u2022 \u2022 , y N },",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "where x m and y n are tokens in the input and output sequence. In text-style conversion, the model predicts the generation probabilities of the output sequence given the input sequence. The generation probability of Y is defined as",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "P (Y |X; \u0398) = N \u220f n=1 P (y n |y 1:n\u22121 , X; \u0398), (1)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "where \u0398 = {\u03b8 enc , \u03b8 dec , \u03b8 lm } represents model parameter sets. \u03b8 enc and \u03b8 dec are trainable parameter sets with encoder and decoder, respectively. \u03b8 lm is parameter set for the external LM. P (y n |y 1:n\u22121 , X; \u0398) can be computed using an encoder and a decoder with memory attentive fusion in the Transformer. Figure 2 shows the Transformer with memory attentive fusion.",
"cite_spans": [],
"ref_spans": [
{
"start": 315,
"end": 323,
"text": "Figure 2",
"ref_id": "FIGREF1"
}
],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "Encoder: The encoder converts an input sequence X into the hidden representations S (K) using K Transformer encoder blocks. First, the input hidden representation of the Transformer encoder block S (0) ",
"cite_spans": [
{
"start": 84,
"end": 87,
"text": "(K)",
"ref_id": null
},
{
"start": 198,
"end": 201,
"text": "(0)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "= {s (0) 1:M } is produced by s (0) m = Embedding(x m ; \u03b8 enc ),",
"eq_num": "(2)"
}
],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "where Embedding(\u2022) consists of positional encoding and a linear layer. Next, the k-th Transformer encoder block composes the k-th hidden representations S (k) from the lower inputs S (k\u22121) as where TransformeEncBlock(\u2022) is the Transformer encoder block that consists of a scaled dot product multi-head self-attention layer and a position-wise feed-forward network (Vaswani et al., 2017) .",
"cite_spans": [
{
"start": 155,
"end": 158,
"text": "(k)",
"ref_id": null
},
{
"start": 183,
"end": 188,
"text": "(k\u22121)",
"ref_id": null
},
{
"start": 364,
"end": 386,
"text": "(Vaswani et al., 2017)",
"ref_id": "BIBREF19"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "S (k) = TransformerEncBlock(S (k\u22121) ; \u03b8 enc ),",
"eq_num": "(3)"
}
],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "Decoder with memory attentive fusion: The decoder with memory attentive fusion computes the generation probability of a token from the preceding tokens and hidden representations of the input sequence and the LM information. The predicted probabilities of the n-th token y n are calculated as",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "P (y n |y 1:n\u22121 , X) = softmax(u (J) n ; \u03b8 dec ),",
"eq_num": "(4)"
}
],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "where softmax(\u2022) is a softmax layer with a linear transformation. The input hidden vector u",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "(J) n",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "is computed from S (K) and y 1:n\u22121 using J Transformer decoder blocks with an external LM. First, the input hidden representation of the Transformer decoder block u",
"cite_spans": [
{
"start": 19,
"end": 22,
"text": "(K)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "n\u22121 and h LM n\u22121 are produced by",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "u (0) n\u22121 = Embedding(y n\u22121 ; \u03b8 dec ),",
"eq_num": "(5)"
}
],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "l LM n\u22121 = LanguageModel(y 1:n\u22121 ; \u03b8 lm ),",
"eq_num": "(6)"
}
],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "h LM n\u22121 = linear(l LM n\u22121 ; \u03b8 dec ),",
"eq_num": "(7)"
}
],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "where LanguageModel(\u2022) is the trained external LM, and l LM n\u22121 is the logit output. Next, we convert hidden representations in the lower layer u (j\u22121) 1:n\u22121 and the encoder output S (k) into a hidden vector c (j) n . The hidden vector is computed as v (j) n = SourceTarget(u",
"cite_spans": [
{
"start": 183,
"end": 186,
"text": "(k)",
"ref_id": null
},
{
"start": 210,
"end": 213,
"text": "(j)",
"ref_id": null
},
{
"start": 253,
"end": 256,
"text": "(j)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "(j\u22121) 1:n\u22121 , u (j\u22121) n\u22121 ; \u03b8 dec ),",
"eq_num": "(8)"
}
],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "v (j) n = LayerNorm(u (j\u22121) n\u22121 +v (j) n ),",
"eq_num": "(9)"
}
],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "c (j) n = SourceTarget(S (K) , v (j) n ; \u03b8 dec ), (10) c (j) n = LayerNorm(v (j) n +c (j) n ),",
"eq_num": "(11)"
}
],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "where SourceTarget(\u2022) is a scaled dot product multi-head source target attention layer and LayerNorm(\u2022) is layer normalization (Ba et al., 2016) . In memory attentive fusion, we also convert the LM output h LM 1:n\u22121 and the hidden vector v ",
"cite_spans": [
{
"start": 127,
"end": 144,
"text": "(Ba et al., 2016)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "b (j) n = SourceTarget(h LM 1:n\u22121 , v (j) n ; \u03b8 dec ), (12) b (j) n = LayerNorm(v (j) n +b (j) n ).",
"eq_num": "(13)"
}
],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "This attention mechanism is repeated with Transformer decoder block in the multi-hop manner. Therefore, we expect to read the memorized memory in the LM effectively. Next, we concatenate the hidden vector that have target and source information, and that have target and the LM information with gating mechanism by",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "g (j) n = sigmoid([c (j) n T , b (j) n T ] T ; \u03b8 dec ), (14) q (j) n = [c (j) n T , g (j) n \u2299 b (j) n T ] T ,",
"eq_num": "(15)"
}
],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "where sigmoid(\u2022) is a sigmoid layer with a linear transformation. Next, the hidden vector q (j) n is converted into the j-th hidden representation u (j) n . The hidden representation is computed as",
"cite_spans": [
{
"start": 92,
"end": 95,
"text": "(j)",
"ref_id": null
},
{
"start": 149,
"end": 152,
"text": "(j)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "u (j) n = FeedForward(q (j) n ; \u03b8 dec ),",
"eq_num": "(16)"
}
],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "u (j) n = LayerNorm(q (j) n +\u016b (j) n ),",
"eq_num": "(17)"
}
],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "where FeedForwrd(\u2022) is a position-wise feedforward network.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "Training: In the Transformer, the model parameter set can be optimized from training dataset",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "D = {(X 1 , Y 1 ), \u2022 \u2022 \u2022 , (X |D| , Y |D| )}.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "The objective function for optimizing the model parameter is defined as",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "L = \u2212 1 |D| |D| \u2211 d=1 log P (Y d |X d ; \u0398).",
"eq_num": "(18)"
}
],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "Note that the external LM uses the freezing parameter \u03b8 lm .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Memory attentive fusion",
"sec_num": "3"
},
{
"text": "We evaluated our method on text-style conversion tasks. In particular, we chose spoken-to-written style conversion task and dialect conversion task in Japanese. In the spoken-to-written style conversion task, spoken-style text produced by an automatic speech recognition system is converted into written-style text that has correct punctuation and no disfluency (Ihori et al., 2020) . In the dialect conversion task, Japanese dialects are converted into standard Japanese.",
"cite_spans": [
{
"start": 362,
"end": 382,
"text": "(Ihori et al., 2020)",
"ref_id": "BIBREF6"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments",
"sec_num": "4"
},
{
"text": "Spoken-to-written style conversion: We used the Corpus of Spontaneous Japanese (CSJ) (Maekawa et al., 2000) and the parallel corpus for Japanese spoken-to-written style conversion (CJSW) (Ihori et al., 2020) . We divided the CSJ into a training set, validation set, and test set. The training set, validation set, and test set have 46,847, 13,510, and 3,949 sentences, respectively. The CJSW has four domains, and we divided it up following (Ihori et al., 2020) . We used all of the training and validation sets for training and each test set (CJSW-1, 2, 3, 4) for the evaluation. All of these datasets are paired data of spoken-style text (manual transcriptions of speech) and writtenstyle text (created with crowd-sourcing).",
"cite_spans": [
{
"start": 85,
"end": 107,
"text": "(Maekawa et al., 2000)",
"ref_id": "BIBREF12"
},
{
"start": 187,
"end": 207,
"text": "(Ihori et al., 2020)",
"ref_id": "BIBREF6"
},
{
"start": 441,
"end": 461,
"text": "(Ihori et al., 2020)",
"ref_id": "BIBREF6"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Datasets",
"sec_num": "4.1"
},
{
"text": "We prepared three paired data of dialects (Tohoku-ben, Osaka-ben, Kyushuben) and standard Japanese with crowd-sourcing. We divided these data into a training set, validation set, and test set for each dialect. We used all of the training and validation sets for training and three test sets for the evaluation. The training set, validation set and test set have 15,506, 3,924 and 2,160 sentences, respectively. Moreover, the test set consists of 700 Tohoku-ben, 862 Osaka-ben, and 598 Kyushu-ben sentences, respectively.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dialect conversion:",
"sec_num": null
},
{
"text": "External text: We prepared a large-scale Japanese web text as the unpaired written-style text data. The Web text was downloaded from various topic Web pages using our home-made crawler. The downloaded pages were filtered in such a way that HTML tags, Javascript codes and other parts that were not useful for these tasks were excluded. Finally, we prepare one million sentences for training the external LM. Moreover, we divided this data into a training set, validation set. The training set and validation set have 800,000 and 200,000 sentences, respectively.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dialect conversion:",
"sec_num": null
},
{
"text": "Transformer: We constructed the Transformer with shallow fusion (Kannan et al., 2018) , cold fusion (Sriram et al., 2018) and memory attentive fusion methods. In addition, we constructed the Transformer without fusion methods as a baseline. We used the following configurations. The dimensions of the output continuous representations and the inner outputs in the position-wise feed-forward network were set to 256, and the number of heads in the multi-head attentions was set to 8. ReLU activation was used in nonlinear transformation function. We stacked 4-layer Transformer encoder blocks, and 2-layer Transformer decoder blocks. We set the output unit size (witch corresponded to the amount of tokens in the training set for language model) to 5,640. To train these models, we used the adam optimizer and label smoothing with a smoothing parameter of 0.1. The training steps were stopped based on early stopping using the part of the training data. We set the mini-batch size to 64 sentences and the dropout rate in the Transformer blocks to 0.2. For the mini-batch training, we truncated each sentence to 200 tokens. We used characters as tokens. All trainable parameters were randomly initialized. For the decoding, we used a beam search algorithm in which the beam size was set to 4.",
"cite_spans": [
{
"start": 64,
"end": 85,
"text": "(Kannan et al., 2018)",
"ref_id": "BIBREF7"
},
{
"start": 100,
"end": 121,
"text": "(Sriram et al., 2018)",
"ref_id": "BIBREF16"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Setups",
"sec_num": "4.2"
},
{
"text": "External LM: We utilized Open AI GPT (Radford et al., 2019) for the LM fusion, although any LM can potentially be used. We used the following configurations. The number of heads in the multi-head attentions was set to 4. We stacked 8layer Transformer blocks. The training steps were stopped based on early stopping using the part of the training data. We set the dropout rate in the Transformer blocks to 0.1. The other settings were the same as the Transformer settings. After training, perplexity of this LM was 11.8. Note that this LM was used in both two tasks and the Transformer and the external LM were not pre-trained. Table 1 shows the experimental results in the spoken-to-written style conversion task. Also, Table 2 shows the experimental results in the dialect conversion task. We calculated automatic evaluation scores in three metrics: BLEU-3 (B-3) (Papineni et al., 2002) , ROUGE-L (R-L) (Lin and Och, 2004) , and METEOR (Banerjee and Lavie, 2005) . Baseline in the tables mean the results of the Transformer without the external LM. Table 1 shows that shallow fusion and cold fusion performed worse than the baseline on the CSJ dataset. On the other hand, memory attentive fusion outperformed the baseline. Moreover, memory attentive fusion outperformed the baseline and shallow fusion on the CJSW dataset. In addition, cold fusion outperformed the baseline on CJSW-1, -3 and -4. As in the spoken-to-written style conversion task, Table 2 shows that memory attentive fusion outperformed the other methods.",
"cite_spans": [
{
"start": 864,
"end": 887,
"text": "(Papineni et al., 2002)",
"ref_id": "BIBREF13"
},
{
"start": 904,
"end": 923,
"text": "(Lin and Och, 2004)",
"ref_id": "BIBREF11"
},
{
"start": 937,
"end": 963,
"text": "(Banerjee and Lavie, 2005)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [
{
"start": 627,
"end": 634,
"text": "Table 1",
"ref_id": "TABREF0"
},
{
"start": 720,
"end": 727,
"text": "Table 2",
"ref_id": "TABREF1"
},
{
"start": 1050,
"end": 1057,
"text": "Table 1",
"ref_id": "TABREF0"
},
{
"start": 1448,
"end": 1455,
"text": "Table 2",
"ref_id": "TABREF1"
}
],
"eq_spans": [],
"section": "Setups",
"sec_num": "4.2"
},
{
"text": "The above results show that shallow fusion is not suitable for the Transformer because it degraded performance in all cases. Moreover, when the LM was integrated with cold fusion, the performance was better than baseline in some domains. Thus, we consider that cold fusion can be used with the Transformer in limited cases. On the other hand, memory attentive fusion outperformed the other fusion methods in almost all of the domains and tasks. Therefore, we consider that memory attentive fusion is suitable for integration of the external LM into the Transformer. In addition, memory attentive fusion worked well especially in the dialect conversion task. Thus, we can infer that the fusion method for the Transformer is more effective when there is small training data.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results",
"sec_num": "4.3"
},
{
"text": "We show the converted example of spoken-towritten style conversion in CSJ dataset with each fusion method in Figure 3 . Figure 3 shows that the word \" \" (flesh) was output correctly with memory attentive fusion, but other methods were not output the word correctly. The word \" \" was not included in training data for the Transformer, but it was included in training data for the external LM. This indicate that only memory attentive fusion was successful in extracting knowledge of the external LM.",
"cite_spans": [],
"ref_spans": [
{
"start": 109,
"end": 117,
"text": "Figure 3",
"ref_id": "FIGREF5"
},
{
"start": 120,
"end": 128,
"text": "Figure 3",
"ref_id": "FIGREF5"
}
],
"eq_spans": [],
"section": "Results",
"sec_num": "4.3"
},
{
"text": "We proposed memory attentive fusion, a novel method to integrate an external LM into the Transformer. Conventional fusion methods assume that the LM is integrated into the RNN-based seq2seq. On the other hand, the proposed method employs a Transformer-specific fusion method which repeats the attention mechanism for the LM many times. Experiments demonstrated that the proposed method outperformed the conventional methods in two tasks. We conclude that the proposed method is suitable for integrating the LM into the Transformer. In the future work, we will try using the proposed method in other natural language generation tasks such as automatic speech recognition.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "5"
}
],
"back_matter": [],
"bib_entries": {
"BIBREF1": {
"ref_id": "b1",
"title": "METEOR: An automatic metric for MT evaluation with improved correlation with human judgments",
"authors": [
{
"first": "Satanjeev",
"middle": [],
"last": "Banerjee",
"suffix": ""
},
{
"first": "Alon",
"middle": [],
"last": "Lavie",
"suffix": ""
}
],
"year": 2005,
"venue": "Proc. the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization",
"volume": "",
"issue": "",
"pages": "65--72",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Satanjeev Banerjee and Alon Lavie. 2005. METEOR: An automatic metric for MT evaluation with im- proved correlation with human judgments. In Proc. the ACL Workshop on Intrinsic and Extrinsic Eval- uation Measures for Machine Translation and/or Summarization, pages 65-72.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Santanu Pal, Matt Post, and Marcos Zampieri. 2019. Findings of the 2019 conference on machine translation (WMT19)",
"authors": [
{
"first": "Lo\u00efc",
"middle": [],
"last": "Barrault",
"suffix": ""
},
{
"first": "Ond\u0159ej",
"middle": [],
"last": "Bojar",
"suffix": ""
},
{
"first": "Marta",
"middle": [
"R"
],
"last": "Costa-Juss\u00e0",
"suffix": ""
},
{
"first": "Christian",
"middle": [],
"last": "Federmann",
"suffix": ""
},
{
"first": "Mark",
"middle": [],
"last": "Fishel",
"suffix": ""
},
{
"first": "Yvette",
"middle": [],
"last": "Graham",
"suffix": ""
},
{
"first": "Barry",
"middle": [],
"last": "Haddow",
"suffix": ""
},
{
"first": "Matthias",
"middle": [],
"last": "Huck",
"suffix": ""
},
{
"first": "Philipp",
"middle": [],
"last": "Koehn",
"suffix": ""
},
{
"first": "Shervin",
"middle": [],
"last": "Malmasi",
"suffix": ""
},
{
"first": "Christof",
"middle": [],
"last": "Monz",
"suffix": ""
},
{
"first": "Mathias",
"middle": [],
"last": "M\u00fcller",
"suffix": ""
}
],
"year": null,
"venue": "Proc. Conference on Machine Translation (WMT)",
"volume": "",
"issue": "",
"pages": "1--61",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Lo\u00efc Barrault, Ond\u0159ej Bojar, Marta R. Costa-juss\u00e0, Christian Federmann, Mark Fishel, Yvette Gra- ham, Barry Haddow, Matthias Huck, Philipp Koehn, Shervin Malmasi, Christof Monz, Mathias M\u00fcller, Santanu Pal, Matt Post, and Marcos Zampieri. 2019. Findings of the 2019 conference on machine trans- lation (WMT19). In Proc. Conference on Machine Translation (WMT), pages 1-61.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Towards better decoding and language model integration in sequence to sequence models",
"authors": [
{
"first": "Jan",
"middle": [],
"last": "Chorowski",
"suffix": ""
},
{
"first": "Navdeep",
"middle": [],
"last": "Jaitly",
"suffix": ""
}
],
"year": 2017,
"venue": "Proc. International Speech Communication Association (INTER-SPEECH)",
"volume": "",
"issue": "",
"pages": "523--527",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jan Chorowski and Navdeep Jaitly. 2017. Towards better decoding and language model integration in sequence to sequence models. In Proc. Interna- tional Speech Communication Association (INTER- SPEECH), pages 523-527.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Speechtransformer: a no-recurrence sequence-to-sequence model for speech recognition",
"authors": [
{
"first": "Linhao",
"middle": [],
"last": "Dong",
"suffix": ""
},
{
"first": "Shuang",
"middle": [],
"last": "Xu",
"suffix": ""
},
{
"first": "Bo",
"middle": [],
"last": "Xu",
"suffix": ""
}
],
"year": 2018,
"venue": "Proc. International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
"volume": "",
"issue": "",
"pages": "5884--5888",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Linhao Dong, Shuang Xu, and Bo Xu. 2018. Speech- transformer: a no-recurrence sequence-to-sequence model for speech recognition. In Proc. International Conference on Acoustics, Speech and Signal Pro- cessing (ICASSP), pages 5884-5888.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "On using monolingual corpora in neural machine translation",
"authors": [
{
"first": "Caglar",
"middle": [],
"last": "Gulcehre",
"suffix": ""
},
{
"first": "Orhan",
"middle": [],
"last": "Firat",
"suffix": ""
},
{
"first": "Kelvin",
"middle": [],
"last": "Xu",
"suffix": ""
},
{
"first": "Kyunghyun",
"middle": [],
"last": "Cho",
"suffix": ""
},
{
"first": "Loic",
"middle": [],
"last": "Barrault",
"suffix": ""
},
{
"first": "Huei-Chi",
"middle": [],
"last": "Lin",
"suffix": ""
},
{
"first": "Fethi",
"middle": [],
"last": "Bougares",
"suffix": ""
},
{
"first": "Holger",
"middle": [],
"last": "Schwenk",
"suffix": ""
},
{
"first": "Yoshua",
"middle": [],
"last": "Bengio",
"suffix": ""
}
],
"year": 2015,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1503.03535"
]
},
"num": null,
"urls": [],
"raw_text": "Caglar Gulcehre, Orhan Firat, Kelvin Xu, Kyunghyun Cho, Loic Barrault, Huei-Chi Lin, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2015. On us- ing monolingual corpora in neural machine transla- tion. arXiv preprint arXiv:1503.03535.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Parallel corpus for Japanese spoken-towritten style conversion",
"authors": [
{
"first": "Mana",
"middle": [],
"last": "Ihori",
"suffix": ""
},
{
"first": "Akihiko",
"middle": [],
"last": "Takashima",
"suffix": ""
},
{
"first": "Ryo",
"middle": [],
"last": "Masumura",
"suffix": ""
}
],
"year": 2020,
"venue": "Proc. Language Resources and Evaluation Conference (LREC)",
"volume": "",
"issue": "",
"pages": "6346--6353",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mana Ihori, Akihiko Takashima, and Ryo Masumura. 2020. Parallel corpus for Japanese spoken-to- written style conversion. In Proc. Language Re- sources and Evaluation Conference (LREC), pages 6346-6353.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "An analysis of incorporating an external language model into a sequence-to-sequence model",
"authors": [
{
"first": "Anjuli",
"middle": [],
"last": "Kannan",
"suffix": ""
},
{
"first": "Yonghui",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Patrick",
"middle": [],
"last": "Nguyen",
"suffix": ""
},
{
"first": "Tara",
"middle": [
"N"
],
"last": "Sainath",
"suffix": ""
},
{
"first": "Zhijeng",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Rohit",
"middle": [],
"last": "Prabhavalkar",
"suffix": ""
}
],
"year": 2018,
"venue": "Proc. International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
"volume": "",
"issue": "",
"pages": "5824--5828",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Anjuli Kannan, Yonghui Wu, Patrick Nguyen, Tara N Sainath, Zhijeng Chen, and Rohit Prabhavalkar. 2018. An analysis of incorporating an exter- nal language model into a sequence-to-sequence model. In Proc. International Conference on Acous- tics, Speech and Signal Processing (ICASSP), pages 5824-5828.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "A comparative study on transformer vs RNN in speech applications",
"authors": [
{
"first": "Shigeki",
"middle": [],
"last": "Karita",
"suffix": ""
},
{
"first": "Nanxin",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Tomoki",
"middle": [],
"last": "Hayashi",
"suffix": ""
},
{
"first": "Takaaki",
"middle": [],
"last": "Hori",
"suffix": ""
},
{
"first": "Hirofumi",
"middle": [],
"last": "Inaguma",
"suffix": ""
},
{
"first": "Ziyan",
"middle": [],
"last": "Jiang",
"suffix": ""
},
{
"first": "Masao",
"middle": [],
"last": "Someki",
"suffix": ""
},
{
"first": "Nelson",
"middle": [
"Enrique"
],
"last": "",
"suffix": ""
},
{
"first": "Yalta",
"middle": [],
"last": "Soplin",
"suffix": ""
},
{
"first": "Ryuichi",
"middle": [],
"last": "Yamamoto",
"suffix": ""
},
{
"first": "Xiaofei",
"middle": [],
"last": "Wang",
"suffix": ""
}
],
"year": 2019,
"venue": "Proc. Automatic Speech Recognition and Understanding Workshop (ASRU)",
"volume": "",
"issue": "",
"pages": "449--456",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Shigeki Karita, Nanxin Chen, Tomoki Hayashi, Takaaki Hori, Hirofumi Inaguma, Ziyan Jiang, Masao Someki, Nelson Enrique Yalta Soplin, Ryuichi Yamamoto, Xiaofei Wang, et al. 2019. A comparative study on transformer vs RNN in speech applications. In Proc. Automatic Speech Recogni- tion and Understanding Workshop (ASRU), pages 449-456.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Entangled transformer for image captioning",
"authors": [
{
"first": "Guang",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Linchao",
"middle": [],
"last": "Zhu",
"suffix": ""
},
{
"first": "Ping",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Yi",
"middle": [],
"last": "Yang",
"suffix": ""
}
],
"year": 2019,
"venue": "Proc. International Conference on Computer Vision (ICCV)",
"volume": "",
"issue": "",
"pages": "8928--8937",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Guang Li, Linchao Zhu, Ping Liu, and Yi Yang. 2019a. Entangled transformer for image captioning. In Proc. International Conference on Computer Vision (ICCV), pages 8928-8937.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "The speechtransformer for large-scale Mandarin Chinese speech recognition",
"authors": [
{
"first": "Jie",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Xiaorui",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Yan",
"middle": [],
"last": "Li",
"suffix": ""
}
],
"year": 2019,
"venue": "Proc. International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
"volume": "",
"issue": "",
"pages": "7095--7099",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jie Li, Xiaorui Wang, Yan Li, et al. 2019b. The speechtransformer for large-scale Mandarin Chinese speech recognition. In Proc. International Confer- ence on Acoustics, Speech and Signal Processing (ICASSP), pages 7095-7099.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics",
"authors": [
{
"first": "Chin-Yew",
"middle": [],
"last": "Lin",
"suffix": ""
},
{
"first": "Franz Josef",
"middle": [],
"last": "Och",
"suffix": ""
}
],
"year": 2004,
"venue": "Proc. Annual Meeting on Association for Computational Linguistics (ACL)",
"volume": "",
"issue": "",
"pages": "605--612",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chin-Yew Lin and Franz Josef Och. 2004. Auto- matic evaluation of machine translation quality us- ing longest common subsequence and skip-bigram statistics. In Proc. Annual Meeting on Association for Computational Linguistics (ACL), pages 605- 612.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Spontaneous speech corpus of Japanese",
"authors": [
{
"first": "Kikuo",
"middle": [],
"last": "Maekawa",
"suffix": ""
},
{
"first": "Hanae",
"middle": [],
"last": "Koiso",
"suffix": ""
},
{
"first": "Sadaoki",
"middle": [],
"last": "Furui",
"suffix": ""
},
{
"first": "Hitoshi",
"middle": [],
"last": "Isahara",
"suffix": ""
}
],
"year": 2000,
"venue": "Proc. International Conference on Language Resources and Evaluation (LREC)",
"volume": "",
"issue": "",
"pages": "947--952",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kikuo Maekawa, Hanae Koiso, Sadaoki Furui, and Hi- toshi Isahara. 2000. Spontaneous speech corpus of Japanese. In Proc. International Conference on Language Resources and Evaluation (LREC), pages 947-952.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Bleu: a method for automatic evaluation of machine translation",
"authors": [
{
"first": "Kishore",
"middle": [],
"last": "Papineni",
"suffix": ""
},
{
"first": "Salim",
"middle": [],
"last": "Roukos",
"suffix": ""
},
{
"first": "Todd",
"middle": [],
"last": "Ward",
"suffix": ""
},
{
"first": "Wei-Jing",
"middle": [],
"last": "Zhu",
"suffix": ""
}
],
"year": 2002,
"venue": "Proc. Annual Meeting on Association for Computational Linguistics (ACL)",
"volume": "",
"issue": "",
"pages": "311--318",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kishore Papineni, Salim Roukos, Todd Ward, and Wei- Jing Zhu. 2002. Bleu: a method for automatic evalu- ation of machine translation. In Proc. Annual Meet- ing on Association for Computational Linguistics (ACL), pages 311-318.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Language models are unsupervised multitask learners. OpenAI blog",
"authors": [
{
"first": "Alec",
"middle": [],
"last": "Radford",
"suffix": ""
},
{
"first": "Jeffrey",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Rewon",
"middle": [],
"last": "Child",
"suffix": ""
},
{
"first": "David",
"middle": [],
"last": "Luan",
"suffix": ""
},
{
"first": "Dario",
"middle": [],
"last": "Amodei",
"suffix": ""
},
{
"first": "Ilya",
"middle": [],
"last": "Sutskever",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI blog, page 9.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Self-attention networks for connectionist temporal classification in speech recognition",
"authors": [
{
"first": "Julian",
"middle": [],
"last": "Salazar",
"suffix": ""
},
{
"first": "Katrin",
"middle": [],
"last": "Kirchhoff",
"suffix": ""
},
{
"first": "Zhiheng",
"middle": [],
"last": "Huang",
"suffix": ""
}
],
"year": 2019,
"venue": "Proc. International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
"volume": "",
"issue": "",
"pages": "7115--7119",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Julian Salazar, Katrin Kirchhoff, and Zhiheng Huang. 2019. Self-attention networks for connectionist tem- poral classification in speech recognition. In Proc. International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 7115-7119.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Cold fusion: Training seq2seq models together with language models",
"authors": [
{
"first": "Anuroop",
"middle": [],
"last": "Sriram",
"suffix": ""
},
{
"first": "Heewoo",
"middle": [],
"last": "Jun",
"suffix": ""
},
{
"first": "Sanjeev",
"middle": [],
"last": "Satheesh",
"suffix": ""
},
{
"first": "Adam",
"middle": [],
"last": "Coates",
"suffix": ""
}
],
"year": 2018,
"venue": "Proc. International Speech Communication Association (IN-TERSPEECH)",
"volume": "",
"issue": "",
"pages": "387--391",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Anuroop Sriram, Heewoo Jun, Sanjeev Satheesh, and Adam Coates. 2018. Cold fusion: Training seq2seq models together with language models. In Proc. In- ternational Speech Communication Association (IN- TERSPEECH), pages 387-391.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "End-to-end memory networks",
"authors": [
{
"first": "Sainbayar",
"middle": [],
"last": "Sukhbaatar",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Weston",
"suffix": ""
},
{
"first": "Rob",
"middle": [],
"last": "Fergus",
"suffix": ""
}
],
"year": 2015,
"venue": "Proc. Advances in neural information processing systems (NIPS)",
"volume": "",
"issue": "",
"pages": "2440--2448",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sainbayar Sukhbaatar, Jason Weston, Rob Fergus, et al. 2015. End-to-end memory networks. In Proc. Advances in neural information processing systems (NIPS), pages 2440-2448.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Sequence to sequence learning with neural networks",
"authors": [
{
"first": "Ilya",
"middle": [],
"last": "Sutskever",
"suffix": ""
},
{
"first": "Oriol",
"middle": [],
"last": "Vinyals",
"suffix": ""
},
{
"first": "Quoc V",
"middle": [],
"last": "Le",
"suffix": ""
}
],
"year": 2014,
"venue": "Proc. Advances in neural information processing systems(NIPS)",
"volume": "",
"issue": "",
"pages": "3104--3112",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural net- works. In Proc. Advances in neural information pro- cessing systems(NIPS), pages 3104-3112.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Attention is all you need",
"authors": [
{
"first": "Ashish",
"middle": [],
"last": "Vaswani",
"suffix": ""
},
{
"first": "Noam",
"middle": [],
"last": "Shazeer",
"suffix": ""
},
{
"first": "Niki",
"middle": [],
"last": "Parmar",
"suffix": ""
},
{
"first": "Jakob",
"middle": [],
"last": "Uszkoreit",
"suffix": ""
},
{
"first": "Llion",
"middle": [],
"last": "Jones",
"suffix": ""
},
{
"first": "Aidan",
"middle": [
"N"
],
"last": "Gomez",
"suffix": ""
},
{
"first": "\u0141ukasz",
"middle": [],
"last": "Kaiser",
"suffix": ""
},
{
"first": "Illia",
"middle": [],
"last": "Polosukhin",
"suffix": ""
}
],
"year": 2017,
"venue": "Proc. Advances in neural information processing systems (NIPS)",
"volume": "",
"issue": "",
"pages": "5998--6008",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proc. Advances in neural information processing systems (NIPS), pages 5998-6008.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Learning deep transformer models for machine translation",
"authors": [
{
"first": "Qiang",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Bei",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Tong",
"middle": [],
"last": "Xiao",
"suffix": ""
},
{
"first": "Jingbo",
"middle": [],
"last": "Zhu",
"suffix": ""
},
{
"first": "Changliang",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Derek",
"middle": [
"F"
],
"last": "Wong",
"suffix": ""
},
{
"first": "Lidia",
"middle": [
"S"
],
"last": "Chao",
"suffix": ""
}
],
"year": 2019,
"venue": "Proc. Association for Computational Linguistics(ACL)",
"volume": "",
"issue": "",
"pages": "1810--1822",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Qiang Wang, Bei Li, Tong Xiao, Jingbo Zhu, Changliang Li, Derek F. Wong, and Lidia S. Chao. 2019. Learning deep transformer models for ma- chine translation. In Proc. Association for Compu- tational Linguistics(ACL), pages 1810-1822.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Multimodal transformer with multi-view visual representation for image captioning",
"authors": [
{
"first": "Jun",
"middle": [],
"last": "Yu",
"suffix": ""
},
{
"first": "Jing",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Zhou",
"middle": [],
"last": "Yu",
"suffix": ""
},
{
"first": "Qingming",
"middle": [],
"last": "Huang",
"suffix": ""
}
],
"year": 2019,
"venue": "IEEE Transactions on Circuits and Systems for Video Technology",
"volume": "",
"issue": "",
"pages": "1--1",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jun Yu, Jing Li, Zhou Yu, and Qingming Huang. 2019. Multimodal transformer with multi-view visual rep- resentation for image captioning. IEEE Transac- tions on Circuits and Systems for Video Technology, pages 1-1.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"text": "Transformer with cold fusion.",
"type_str": "figure",
"num": null,
"uris": null
},
"FIGREF1": {
"text": "Transformer with memory attentive fusion.",
"type_str": "figure",
"num": null,
"uris": null
},
"FIGREF2": {
"text": "(j) n into a hidden vector b (j) n with attention mechanism. The hidden vector is computed as",
"type_str": "figure",
"num": null,
"uris": null
},
"FIGREF3": {
"text": "Baseline b). Shallow fusion c). Cold fusion d). Memory attentive fusion",
"type_str": "figure",
"num": null,
"uris": null
},
"FIGREF4": {
"text": "b). Shallow fusion c). Cold fusion d). Memory attentive fusion",
"type_str": "figure",
"num": null,
"uris": null
},
"FIGREF5": {
"text": "Example of spoken-to-written style conversion in CSJ dataset with each fusion method.",
"type_str": "figure",
"num": null,
"uris": null
},
"TABREF0": {
"content": "<table/>",
"num": null,
"html": null,
"type_str": "table",
"text": "Results on spoken-to-written style conversion tasks."
},
"TABREF1": {
"content": "<table/>",
"num": null,
"html": null,
"type_str": "table",
"text": "Results on dialect conversion tasks."
}
}
}
} |