| { | |
| "paper_id": "I11-1004", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T07:31:37.047717Z" | |
| }, | |
| "title": "Extracting Pre-ordering Rules from Predicate-Argument Structures", | |
| "authors": [ | |
| { | |
| "first": "Xianchao", | |
| "middle": [], | |
| "last": "Wu", | |
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| "institution": "NTT Corporation", | |
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| "addrLine": "2-4 Hikaridai Seika-cho, Soraku-gun Kyoto", | |
| "postCode": "619-0237", | |
| "country": "Japan" | |
| } | |
| }, | |
| "email": "wu.xianchao@lab.ntt.co.jp" | |
| }, | |
| { | |
| "first": "Katsuhito", | |
| "middle": [], | |
| "last": "Sudoh", | |
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| "institution": "NTT Corporation", | |
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| "addrLine": "2-4 Hikaridai Seika-cho, Soraku-gun Kyoto", | |
| "postCode": "619-0237", | |
| "country": "Japan" | |
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| }, | |
| "email": "sudoh.katsuhito@lab.ntt.co.jp" | |
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| { | |
| "first": "Kevin", | |
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| "country": "Japan" | |
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| "email": "kevin.duh@lab.ntt.co.jp" | |
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| { | |
| "first": "Hajime", | |
| "middle": [], | |
| "last": "Tsukada", | |
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| "addrLine": "2-4 Hikaridai Seika-cho, Soraku-gun Kyoto", | |
| "postCode": "619-0237", | |
| "country": "Japan" | |
| } | |
| }, | |
| "email": "tsukada.hajime@lab.ntt.co.jp" | |
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| { | |
| "first": "Masaaki", | |
| "middle": [], | |
| "last": "Nagata", | |
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| "addrLine": "2-4 Hikaridai Seika-cho, Soraku-gun Kyoto", | |
| "postCode": "619-0237", | |
| "country": "Japan" | |
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| }, | |
| "email": "nagata.masaaki@lab.ntt.co.jp" | |
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| "year": "", | |
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| "abstract": "Word ordering remains as an essential problem for translating between languages with substantial structural differences, such as SOV and SVO languages. In this paper, we propose to automatically extract pre-ordering rules from predicateargument structures. A pre-ordering rule records the relative position mapping of a predicate word and its argument phrases from the source language side to the target language side. We propose 1) a lineartime algorithm to extract the pre-ordering rules from word-aligned HPSG-tree-tostring pairs and 2) a bottom-up algorithm to apply the extracted rules to HPSG trees to yield target language style source sentences. Experimental results are reported for large-scale English-to-Japanese translation, showing significant improvements of BLEU score compared with the baseline SMT systems.", | |
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| "text": "Word ordering remains as an essential problem for translating between languages with substantial structural differences, such as SOV and SVO languages. In this paper, we propose to automatically extract pre-ordering rules from predicateargument structures. A pre-ordering rule records the relative position mapping of a predicate word and its argument phrases from the source language side to the target language side. We propose 1) a lineartime algorithm to extract the pre-ordering rules from word-aligned HPSG-tree-tostring pairs and 2) a bottom-up algorithm to apply the extracted rules to HPSG trees to yield target language style source sentences. Experimental results are reported for large-scale English-to-Japanese translation, showing significant improvements of BLEU score compared with the baseline SMT systems.", | |
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| "text": "Statistical machine translation (SMT) suffers from an essential problem for translating between languages with substantial structural differences, such as between English which is a subject-verbobject (SVO) language and Japanese which is a typical subject-object-verb (SOV) language.", | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
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| "text": "Numerous approaches have been consequently proposed to tackle this word-order problem, such as lexicalized reordering methods, syntax-based models, and pre-ordering ways. First, in order to overcome the shortages of traditional distance based distortion models (Brown et al., 1993; , phrase dependent lexicalized reordering models were proposed by several researchers (Tillman, 2004; Kumar and Byrne, 2005) . Lexicalized reordering models learn local orientations (monotone or reordering) with probabilities for each bilingual phrase from the training data. For example, by taking lexical information as features, a maximum entropy phrase reordering model was proposed by Xiong et al. (2006) .", | |
| "cite_spans": [ | |
| { | |
| "start": 261, | |
| "end": 281, | |
| "text": "(Brown et al., 1993;", | |
| "ref_id": "BIBREF0" | |
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| { | |
| "start": 368, | |
| "end": 383, | |
| "text": "(Tillman, 2004;", | |
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| "end": 406, | |
| "text": "Kumar and Byrne, 2005)", | |
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| "text": "Xiong et al. (2006)", | |
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| "text": "Second, syntax-based models attempt to solve the word ordering problem by employing syntactic structures. For example, linguistically syntaxbased approaches (Galley et al., 2004; first parse source and/or target sentences and then learn reordering templates from the subtree fragments of the parse trees. In contrast, hierarchical phrase based translation (Chiang, 2005 ) is a formally syntax-based approach which can automatically extract hierarchical ordering rules from aligned string-string pairs without using additional parsers. These approaches have been proved to be both algorithmically appealing and empirically successful.", | |
| "cite_spans": [ | |
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| "text": "(Galley et al., 2004;", | |
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| "text": "However, most of current syntax-based SMT systems use IBM models (Brown et al., 1993 ) and hidden Markov model (HMM) (Vogel et al., 1996) to generate word alignments. These models have a penalty parameter associated with long distance jumps, and tend to misalign words which move far from the window sizes of their expected positions (Xu et al., 2009; Genzel, 2010) .", | |
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| "text": "(Vogel et al., 1996)", | |
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| "text": "The third type tackles the word-order problem in pre-ordering ways. Through the usage of a sequence of pre-ordering rules, the word order of an original source sentence is (approximately) changed into the word order of the target sentence. Here, the pre-ordering rules can be manually or automatically extracted. For manual extraction of pre-ordering rules, linguistic background and expertise are required for predetermined language pairs, such as for German-English (Collins et al., 2005) , Chinese-to-English (Wang et al., 2007) , Japanese-to-English (Katz-Brown and , and English-to-SOV languages (Xu et al., 2009) . Specially, for English-to-Japanese translation, Isozaki et al. (2010b) proposed to move syntactic or semantic heads to the end of corresponding phrases or clauses so that to yield head finalized English (HFE) sentences which follow the word order of Japanese. The head information of an English sentence is detected by a head-driven phrase structure grammar (HPSG) parser, Enju 1 (Miyao and Tsujii, 2008) . In addition, transformation rules were manually written for appending particle seed words, refining POS tags to be used before parsing, and deleting English determiners. Due to the usage of the same parser, we take this HFE approach as one of our baseline systems.", | |
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| "text": "The goal in this paper, however, is to learn preordering rules from parallel data in an automatic way. Under this motivation, pre-ordering rules can be extracted in a language-independent manner. A number of researches follow this automatic way. For example, in (Xia and McCord, 2004) , a variety of heuristic rules were applied to bilingual parse trees to extract pre-ordering rules for French-English translation. Rottmann and Vogen (2007) learned reordering rules based on sequences of part-of-speech (POS) tags, instead of parse trees. Dependency trees were used by Genzel (2010) to extract source-side reordering rules for translating languages from SVO to SOV, etc..", | |
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| "text": "The novel idea expressed in this paper is that, predicate-argument structures (PASs) are introduced to extract fine-grained pre-ordering rules. PASs have the following merits for describing reordering phenomena:", | |
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| "text": "\u2022 predicate words and argument phrases respectively record reordering phenomena in a lexicalized level and an abstract level;", | |
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| "text": "\u2022 PASs provide a fine-grained classification of the reordering phenomena since they include factored representations of syntactic features of the predicate words and their argument phrases.", | |
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| "text": "The idea of using PASs for pre-ordering follows (Komachi et al., 2006) . Several reordering operations were manually designed by Komachi et al. (2006) to pre-ordering Japanese sentences into SVO-style English sentences. For comparison, our proposal 1) makes use of not only PASs but also the source syntactic tree structures for preordering rule matching, 2) extracts pre-ordering 1 http://www-tsujii.is.s.u-tokyo.ac.jp/enju/index.html rules in an automatic way, and 3) use factored representations of syntactic features to refine the preordering rules.", | |
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| "text": "Following (Wu et al., 2010a; Isozaki et al., 2010b) , we use the HPSG parser Enju to generate the PASs of English sentences. HPSG (Pollard and Sag, 1994 ) is a lexicalist grammar framework. In HPSG, linguistic entities such as words and phrases are represented by a data structure called a sign. A sign gives a factored representation of the syntactic features of a word/phrase, as well as a representation of their semantic content which corresponds to PASs.", | |
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| "text": "In order to record the relative positions among a predicate word and its argument phrases, we propose a linear-time algorithm to extract preordering rules from word-aligned HPSG-tree-tostring pairs 2 . The syntactic features included in signs and the types of PASs enable us to extract fine-grained pre-ordering rules and thus make it easier to select appropriate rules for given source HPSG trees. We further propose a bottom-up algorithm to apply the extracted rules to HPSG trees to pre-order source sentences. Using the preordered source sentences, we retrain word alignments again.", | |
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| "text": "The remaining of this paper is organized as follows. In the next section, we describe the algorithms guided by using a real example for extracting and applying PAS-based pre-ordering rules. Then, we design experiments on large-scale English-to-Japanese translation to testify our proposal. Employing Moses , we show that our proposal can significantly improve BLEU scores of 2.47\u223c3.15 points compared with using the original English sentences. We finally conclude this paper by summarizing our proposal and the experiment results. denote non-terminal nodes (e.g., c0, c1), and the identifiers that start with 't' denote terminal nodes (e.g., t0, t2). In a complete HPSG tree (Wu et al., 2010b) , factored syntactic features listed in Table 1 are included in the terminal and nonterminal signs. These features are used by us to sub-categorize pre-ordering rules. As an example of the XML output of Enju, the signs of \"when\" (t0) and its arguments c16, c3 are shown in the top-left corner of Figure 1.", | |
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| "text": "\u6d41 \u4f53 0 \u5727 1 \u30b7 \u30ea \u30f3 \u30c0 \u308c \u308b 12 \u3053 \u3068 13 31 3 \u306e 4 \u5834 \u5408 5 \u306f 6 \u6d41 \u4f53 7 \u304c 8 \u5f90 \u3005 \u306b 9 \u6392 \u51fa 10 \u3055 11 \u306a", | |
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| "text": "We define the following data structures for both extracting and applying pre-ordering rules. First, a PAS-based pre-ordering rule is defined to be a four-tuple <pw, args, srcOrder, trgOrder>. Here, pw is the predicate word, args are the argument nodes of pw, and srcOrder and trgOrder respectively record the relative positions among pw and args in the source and target language sides. Then, we suppose an HPSG tree/subtree object contains the following methods:", | |
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| "sec_num": "2.2" | |
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| "text": "\u2022 localize(): localize syntactic/semantic heads;", | |
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| "text": "\u2022 computeSrcSpans(): topologically compute the source span of each node;", | |
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| "text": "\u2022 computeSpans(A): topologically compute the source and target spans of each node (Galley et al., 2004) . A is the word alignment;", | |
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| "text": "\u2022 getArgs(pw): return the argument nodes of pw;", | |
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| "text": "Name Description Examples WORD surface word form \"when\" BASE base word form \"when\" POS part-of-speech WRB (\"when\") LE lexical entry [when] (\"when\") PRED type of predicate conj arg12 argument structure (\"when\") CAT syntactic category SC (\"when\") TENSE tense of a verb (past, present (\"used\") present, untensed) ASPECT aspect of a verb none (\"used\") (none, prefect, progressive, prefect-progressive) VOICE voice of a verb passive (\"used\") (passive, active) AUX auxiliary verb or not minus (\"used\") (minus, modal, have, be, do, to, copular) CAT syntactic category S (c16), S (c3) XCAT extended category HEAD syntactic head R (c16), R(c3) SEM HEAD semantic head R (c16), R (c3) SCHEMA schema rule mod head (c16) \u2022 MCT(pw, args): return the minimum cover tree (Wu et al., 2010a) of pw and args.", | |
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| "text": "To implement the localize() method, we use the approach described in (Wu et al., 2010a) . That is, we replace the pointer values of HEAD and SEM HEAD features in non-terminal nodes with three labels: \"S\" for single daughter, \"L\" for the left-hand-side daughter, and \"R\" for the right-hand-side daughter. For example, for node c16 in Figure 1 , its HEAD and SEM HEAD will change from c18 to \"R\".", | |
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| "text": "Figure 1", | |
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| "text": "We use the concept of minimum covering trees (MCT) defined in (Wu et al., 2010b) to guide the pre-ordering process. A MCT is a subtree of the original HPSG tree that takes a predicate node and its argument nodes as (new) leaf nodes. For example, as shown in the top-right corner of Figure 1 , the MCT of \"when\" (t0) and its argument nodes c3, c16 is \"c0(c1(c2(t0)c3)c16)\".", | |
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| "text": "Finally, the attributes in the nodes of an HPSG tree include: 1) pred: the PAS of a leaf node, 2) srcSpan: the index set of the source words that current node covers, 3) trgSpan: the index set of the target words that srcSpan aligned to, and 4) sr-cPhrase that stores the pre-ordered source phrase covered by current node.", | |
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| "text": "We express the idea for extracting PAS-based preordering rules by using the first word \"when\" of the English sentence in Figure 1 . Given the PAS information of \"when\" (t0) in the English side, we need to determine the target-side-order among t0 and its two arguments c16, c3. To achieve this, we compute the target spans of these three nodes by using current word alignment and then sort their target spans. Through referring to the word alignment shown in Figure 1 , we can collect the target spans which are {5}, {4,0,1,2,3,6,15}, and {7,8,9,10,11,12,13} respectively for t0, c3, and c16. However, we cannot sort these three spans since there are overlapping between the first two spans 3 . In order to solve this problem, we sort the spans in a heuristic way. Note that in c3's target span, five indices are smaller than 5 yet only two indices are larger than 5. Thus, we take {4,0,1,2,3,6,15} to be dominantly smaller than {5}. Now, we can determine the pre-order rule guided by the PAS of t0 to be \"t0 c3 c16 \u2192 c3 t0 c16\" and formally to be \"t0 0 c3 1 c16 2 \u2192 1 0 2\". Generally, we use the following heuristic rules to sort two spans, named span A and span B:", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 121, | |
| "end": 129, | |
| "text": "Figure 1", | |
| "ref_id": "FIGREF0" | |
| }, | |
| { | |
| "start": 458, | |
| "end": 466, | |
| "text": "Figure 1", | |
| "ref_id": "FIGREF0" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Rule extraction algorithm", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "\u2022 if more than half of numbers in A is bigger than the maximum number in B, or if more than half of numbers in B is smaller than the minimum number in A, then B < A;", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Rule extraction algorithm", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "Algorithm 1 Pre-ordering Rule Extraction", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Rule extraction algorithm", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "Input: HPSG tree TE of an English sentence E, word alignment A Output: a pre-ordering rule set R 1: TE.localize() 2: TE.computeSpans(A) 3: for each leaf node t of TE do 4:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Rule extraction algorithm", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "if t.pred is opened and t.trgSpan != NULL then 5:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Rule extraction algorithm", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "Node[] args \u2190 TE.getArgs(t) 6:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Rule extraction algorithm", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "if all nodes in args are aligned then 7:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Rule extraction algorithm", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "int[] srcOrder \u2190 SORTSPANS(t.srcSpan, src-Spans of args) 8:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Rule extraction algorithm", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "int[] trgOrder \u2190 SORTSPANS(t.trgSpan, trgSpans of args) 9:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Rule extraction algorithm", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "R.add(< t, args, srcOrder, trgOrder>) 10:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Rule extraction algorithm", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "end if 11:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Rule extraction algorithm", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "end if 12: end for", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Rule extraction algorithm", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "\u2022 if more than half of numbers in B is bigger than the maximum number in A, or if more than half of numbers in A is smaller than the minimum number in B, then A < B.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Rule extraction algorithm", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "In case of a tie (e.g., A={3,4,7,8}, B={5,6}), we keep the original order of A and B in the sourceside sentence without any reordering. Algorithm 1 sketches the pre-ordering rule extraction algorithm guided by PASs. The algorithm collect pre-ordering rules through a traversal of the leaf nodes in an HPSG tree. A non-terminal node will not be accessed unless it is an argument of some predicate node(s). Thus, this algorithm runs in a time that is approximately linear to the number of leaf nodes in the tree, i.e., the number of words in the source sentence.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Rule extraction algorithm", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "We define that a terminal node's PAS is opened if at least one of its arguments is neither empty nor unknown. We will not extract a pre-ordering rule if the terminal node is unaligned or any of its argument node is unaligned. These constraints are reflected by Line 4 and 6 in Algorithm 1. After heuristically sorting the source/target spans of a predicate node and its argument nodes, we finally extract a pre-ordering rule. Table 2 summarizes the PAS-based pre-ordering rules extracted from the example shown in Figure 1. Application of these pre-ordering rules to the original English sentence yields the following Japanese style sentence:", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 426, | |
| "end": 433, | |
| "text": "Table 2", | |
| "ref_id": "TABREF4" | |
| }, | |
| { | |
| "start": 514, | |
| "end": 520, | |
| "text": "Figure", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Rule extraction algorithm", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "\u2022 the fluid pressure cylinder 31 is used when, fluid is gradually applied. Algorithm 2 sketches the algorithm for applying pre-ordering rules to a given HPSG tree T E . The algorithm contains three parts: rule matching (Lines 4-12), bottom-up rule applying (Lines 13-19), and sentence collecting (Lines 20-26). We first retrieve available pre-ordering rules from rule set R by a left-to-right traversal of the leaf nodes of T E . For each leaf node, we select one preordering rule with the highest frequency. Our experiments testified that this greedy rule selection strategy worked quite well. We selected 93% of the top frequent rule without facing a tie.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Rule extraction algorithm", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "The terminal node t, the argument nodes of t, and their source-side ordering are taken as the key for rule matching. Available rules will be assigned to the MCT of t. Then, we apply the available rules to the root nodes of each MCT through a bottom-up traversal of T E . A competitive problem is that, a non-terminal node can be shared by several MCTs. For example, node c3 and c18 (gray color) in Figure 1 are respectively shared by two MCTs (t6 and t7, t10 and t12). In order to avoid duplicated reordering of these nodes, we first pick the pre-ordering rule in which there are no \"gaps\" among the predicate words and argument phrases. For example, there is a gap (t6) between t7 and its argument node c4. We then pick a rule by frequency if there are still more than one rule available. Finally, after applying all available rules, we collect the pre-ordered source sentence from the root node of the HPSG tree.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 398, | |
| "end": 406, | |
| "text": "Figure 1", | |
| "ref_id": "FIGREF0" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Applying pre-ordering rules", | |
| "sec_num": "2.4" | |
| }, | |
| { | |
| "text": "We test our proposal by translating from English to Japanese. We use the NTCIR-9 English-Japanese patent corpus 4 as our experiment set. Since the reference set of the official test set has not been released yet, we instead split the original development set averagely into two parts, named dev.a and dev.b. In our experiments, we first take dev.a as our development set for minimum-error rate tuning (Och, 2003) and then report the final translation accuracies on dev.b. For direct comparison with other systems in the future, we use the configuration of the official baseline system 5 :", | |
| "cite_spans": [ | |
| { | |
| "start": 401, | |
| "end": 412, | |
| "text": "(Och, 2003)", | |
| "ref_id": "BIBREF15" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Setup", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "\u2022 Moses 6 : revision = \"3717\" as the baseline decoder. Note that we also train Moses using HFE sentences (Isozaki et al., 2010b) and the English sentences pre-ordered by PASs;", | |
| "cite_spans": [ | |
| { | |
| "start": 105, | |
| "end": 128, | |
| "text": "(Isozaki et al., 2010b)", | |
| "ref_id": "BIBREF6" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Setup", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "\u2022 GIZA++: giza-pp-v1.0.3 7 (Och and Ney, 2003) for first training word alignment using the original English sentences for preordering rule extraction, and then for retrain- ing word alignments using the pre-ordered English sentences;", | |
| "cite_spans": [ | |
| { | |
| "start": 27, | |
| "end": 46, | |
| "text": "(Och and Ney, 2003)", | |
| "ref_id": "BIBREF14" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Setup", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "\u2022 SRILM 8 (Stolcke, 2002) : version 1.5.12 for training a 5-gram language model using the target sentences in the total training set;", | |
| "cite_spans": [ | |
| { | |
| "start": 10, | |
| "end": 25, | |
| "text": "(Stolcke, 2002)", | |
| "ref_id": "BIBREF19" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Setup", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "\u2022 Additional scripts 9 : for preprocessing English sentences and cleaning up too long (# of words > 40) parallel sentences;", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Setup", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "\u2022 Japanese word segmentation: Mecab v0.98 10 with the dictionary of mecab-ipadic-2.7.0-20070801.tar.gz 11 .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Setup", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "The statistics of the filtered training set, dev.a, and dev.b are shown in Table 3 . The success parsing rate ranges from 98.7% to 99.3% by using Enju2.3.1. The averaged parsing time for each English sentence ranges from 0.30 to 0.48 seconds.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 75, | |
| "end": 82, | |
| "text": "Table 3", | |
| "ref_id": "TABREF6" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Setup", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "pre-ordering rules Figure 2 shows the number (natural log) of the 40 types of the PASs that appeared in the HPSG trees of the three experiment sets. Top five types of opened PASs include adj arg1, det arg1, prep arg12, noun arg1, and verb arg12. By comparing the distributions of the number of PASs in the three sets, we can see that the distributions approximately share the same tendency. Thus, the pre-ordering rules learned from the PASs in the training set can be expected to be properly applied in dev.a and dev.b. Besides, the statistics of the number of arguments for the predicate words is shown in Table 4 . From this table, we find that the ratio of the number of arguments in the three sets are approximately similar. In particular, nearly half of the predicate words have one argument. The number of predicate words that contain two arguments occurs around 30.0% of all the predicate words. Also, we can not extract pre-ordering rules from around 23.0% of the predicate words since they do not contain any arguments. Finally, less than 1% of predicate words contain three arguments and we only find one four-argument example of verb arg1234 in the training set. Now, in Table 5 , we show the statistics of predicate words in the training set for pre-ordering rule extraction. Of the 48.3 million English words in the training set, there are 45.6 million words (94.4%) that are included in the HPSG trees that were successfully generated. Then, in the PASs of these 45.6 million words, there are 35.0 million words whose PASs are opened. We also list the number (34.0 million) of aligned predicate words, since we only extract pre-ordering rules from predicate words that are aligned to some target word(s) in Algorithm 1. Finally, there are 89.1% of aligned predicate words that are aligned to contiguous target words.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 19, | |
| "end": 27, | |
| "text": "Figure 2", | |
| "ref_id": "FIGREF1" | |
| }, | |
| { | |
| "start": 608, | |
| "end": 616, | |
| "text": "Table 4", | |
| "ref_id": "TABREF8" | |
| }, | |
| { | |
| "start": 1184, | |
| "end": 1191, | |
| "text": "Table 5", | |
| "ref_id": "TABREF9" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Statistics of PASs and PAS-based", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "In order to investigate the sub-categorization effectiveness of the syntactic features included in the pre-ordering rules, we pick four subsets of the total feature set (Table 1) . These feature subsets, named from PAS-a to PAS-d, are listed in Table 6 . Through the comparison of these four feature subsets, we also attempt to investigate the datasparseness problem of available pre-ordering rules cased by the factored features.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 169, | |
| "end": 178, | |
| "text": "(Table 1)", | |
| "ref_id": "TABREF2" | |
| }, | |
| { | |
| "start": 245, | |
| "end": 253, | |
| "text": "Table 6", | |
| "ref_id": "TABREF11" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Statistics of PASs and PAS-based", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "PAS-a includes all the syntactic features listed in Table 1 . In PAS-b, we only keep three features for the predicate word and one feature for the argu -0 2 4 6 8 10 12 14 16 18 adj_arg1 adj_arg12 adj_mod_arg1 adj_mod_arg12 app_arg12 aux_arg12 aux_mod_arg12 comp_arg1 comp_arg12 conj_arg1 conj_arg12 conj_arg123 coord_arg12 det_arg1 dtv_arg2 it_arg1 lgs_arg2 lparen_arg123 noun_arg0 noun_arg1 noun_arg12 noun_arg2 poss_arg12 poss_arg2 prep_arg12 prep_arg123 prep_mod_arg12 punct_arg1 quote_arg23 relative_arg1 relative_arg12 rparen_arg0 there_arg0 verb_arg1 verb_arg12 verb_arg123 verb_arg1234 verb_mod_arg1 verb_mod_arg12 verb_mod_arg123 train (ln) dev.a (ln) dev.b (ln) Table 6 : Feature subsets used in pre-ordering rules and statistics of the extraction and application of the pre-ordering rules under these feature subsets. ment nodes. We further remove one feature (CAT) of the predicate word in PAS-c. In the fourth subset PAS-d, we only use two features WORD and PRED in the predicate word for sub-categorizing pre-ordering rules. Thus, PAS-d is only related to PASs (which can be generated by any kinds of parser) since it does not include additional features generated by the typical HPSG parser.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 52, | |
| "end": 59, | |
| "text": "Table 1", | |
| "ref_id": "TABREF2" | |
| }, | |
| { | |
| "start": 152, | |
| "end": 697, | |
| "text": "-0 2 4 6 8 10 12 14 16 18 adj_arg1 adj_arg12 adj_mod_arg1 adj_mod_arg12 app_arg12 aux_arg12 aux_mod_arg12 comp_arg1 comp_arg12 conj_arg1 conj_arg12 conj_arg123 coord_arg12 det_arg1 dtv_arg2 it_arg1 lgs_arg2 lparen_arg123 noun_arg0 noun_arg1 noun_arg12 noun_arg2 poss_arg12 poss_arg2 prep_arg12 prep_arg123 prep_mod_arg12 punct_arg1 quote_arg23 relative_arg1 relative_arg12 rparen_arg0 there_arg0 verb_arg1 verb_arg12 verb_arg123 verb_arg1234 verb_mod_arg1 verb_mod_arg12 verb_mod_arg123", | |
| "ref_id": "TABREF2" | |
| }, | |
| { | |
| "start": 731, | |
| "end": 738, | |
| "text": "Table 6", | |
| "ref_id": "TABREF11" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Statistics of PASs and PAS-based", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "As the number of syntactic features decreases, more rules can be unified together. Thus, the number of pre-ordering rules and reordering rules, as shown in Table 7 shows the final translation accuracies under BLEU score (Papineni et al., 2002) and RIBES 12 , i.e., the software implementation of Normalized Kendall's \u03c4 as proposed by (Isozaki et al., 2010a) to automatically evaluate the translation between distant language pairs based on rank correlation coefficients and significantly penalizes word order mistakes. Making use of our preordered English sentences significantly (p < 0.01) improved BLEU scores from 2.47 (PAS-d) to 3.15 (PAS-a) points. The effectiveness of our proposal for tackling word-ordering problem can also be proved by comparing the scores of RIBES. In addition, the accuracies change slightly among using the four types of pre-ordering rules. Among PAS-a, PAS-b, and PAS-c, we did significant test and could not differ them under p < 0.01 or p < 0.05. The only significant difference Table 8 : Translation accuracies by combining HFE and PAS based pre-ordering approach.", | |
| "cite_spans": [ | |
| { | |
| "start": 220, | |
| "end": 243, | |
| "text": "(Papineni et al., 2002)", | |
| "ref_id": "BIBREF16" | |
| }, | |
| { | |
| "start": 334, | |
| "end": 357, | |
| "text": "(Isozaki et al., 2010a)", | |
| "ref_id": "BIBREF5" | |
| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 156, | |
| "end": 163, | |
| "text": "Table 7", | |
| "ref_id": "TABREF12" | |
| }, | |
| { | |
| "start": 1011, | |
| "end": 1018, | |
| "text": "Table 8", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Statistics of PASs and PAS-based", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "(p < 0.05) appeared between PAS-a and PAS-d. Thus, we argue that the factored syntactic features such as WORD, PRED, and CAT are more essential for sub-categorizing pre-ordering rules than the remaining syntactic features.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Results", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "As former mentioned, we also take the language-dependent HFE approach (Isozaki et al., 2010b) as another baseline. Note that word alignment was retrained using head-finalized English sentences and Japanese sentences in this HFE approach. Through comparing the HFE results listed in Table 8 , we observe that the results are comparable between PAS-a and HFE: HFE is slightly better under BLEU score and PAS-a is slightly better under RIBES score.", | |
| "cite_spans": [ | |
| { | |
| "start": 70, | |
| "end": 93, | |
| "text": "(Isozaki et al., 2010b)", | |
| "ref_id": "BIBREF6" | |
| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 282, | |
| "end": 289, | |
| "text": "Table 8", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Results", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "Since similar HPSG parser (Enju) yet different linguistic information (syntactic head information vs. PASs) are used in HFE approach and our proposal. A straightforward question is whether we can combine these approaches together. Under this motivation, we select a better pre-ordered English sentence generated by the HFE method and our PAS-based method. Following (Genzel, 2010) , we use crossing score as the metric for sentence selection. Crossing score is the number of crossing alignment links for a given aligned sentence pair. For monotonic alignments without reordering, crossing score is zero. During selection, we found that nearly 10% of the pre-ordered English sentences yielded by head-finalization and PAS-based methods were similar. In addition, among the different sentences, around 30% of PAS-based pre-ordering sentences were selected. Since we can not compute crossing score in the development/test sets, we instead take both kinds of pre-ordered English sentences as inputs and pick one output with a higher translation score.", | |
| "cite_spans": [ | |
| { | |
| "start": 366, | |
| "end": 380, | |
| "text": "(Genzel, 2010)", | |
| "ref_id": "BIBREF4" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Results", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "The translation result based on this reselection approach is shown in Table 8 . Compared with HFE approach, the reselection approach significantly (p < 0.01) improved BLEU scores of from 1.22 (PAS-d) to 1.68 (PAS-b) points. These interesting results reflect that syntactic head infor- mation and PASs describe the linguistic information of an English sentence in different aspects. Furthermore, compared with the single headfinalization rule, the automatically extracted preordering rules kept the variety of word-ordering by dynamically inferring the word order of target sentences and thus enlarged the reordering space.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 70, | |
| "end": 77, | |
| "text": "Table 8", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Results", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "In order to investigate how closely the pre-ordered English sentences follow target language word order, we measured Kendall's \u03c4 (Kendall, 1948) , a rank correlation coefficient, as shown in Table 9 . We exactly follow Isozaki et al. (2010b) to compute Kendall's \u03c4 . From Table 9 , we can see that the quality of word alignments approximately reflects the final BLEU scores listed in Table 7 and 8.", | |
| "cite_spans": [ | |
| { | |
| "start": 129, | |
| "end": 144, | |
| "text": "(Kendall, 1948)", | |
| "ref_id": "BIBREF8" | |
| }, | |
| { | |
| "start": 219, | |
| "end": 241, | |
| "text": "Isozaki et al. (2010b)", | |
| "ref_id": "BIBREF6" | |
| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 191, | |
| "end": 198, | |
| "text": "Table 9", | |
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| }, | |
| { | |
| "start": 272, | |
| "end": 279, | |
| "text": "Table 9", | |
| "ref_id": "TABREF15" | |
| }, | |
| { | |
| "start": 384, | |
| "end": 391, | |
| "text": "Table 7", | |
| "ref_id": "TABREF12" | |
| } | |
| ], | |
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| "section": "Alignment comparison", | |
| "sec_num": "3.4" | |
| }, | |
| { | |
| "text": "We have proposed a pre-ordering approach by making use of predicate argument structures. The pre-ordering rules record the relative source-target position mapping among predicate words and their argument phrases. We first proposed an algorithm for automatically extracting these lexical pre-ordering rules from aligned HPSG-tree-tostring pairs. Then, we apply these pre-ordering rules to HPSG trees to yield pre-ordered source sentences that follow the word order of target sentences. Finally, we do word alignment again by using the pre-ordered source sentences together with the original target sentences. Employing Moses , our proposal significantly improved 2.47\u223c3.15 BLEU points compared with using the original English sentences. Combining with the HFE approach (Isozaki et al., 2010b) , our approach significantly and impressively improved 5.29 points of BLEU score from 0.2773 to 0.3302. We finally argue that our proposal is not difficult to be implemented and can be easily applied to translate English into other languages.", | |
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| "start": 768, | |
| "end": 791, | |
| "text": "(Isozaki et al., 2010b)", | |
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| "section": "Conclusion", | |
| "sec_num": "4" | |
| }, | |
| { | |
| "text": "These word alignments are gained by running GIZA++(Och and Ney, 2003) on the original parallel sentences.", | |
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| "text": "In this example, the overlapping is caused by the wrong/ambiguous alignments between \"used\" and \"naru15\", and between \"is\" and \"ha6\".", | |
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| "text": "http://ntcir.nii.ac.jp/PatentMT/ 5 http://ntcir.nii.ac.jp/PatentMT/baselineSystems 6 http://www.statmt.org/moses/ 7 http://giza-pp.googlecode.com/files/giza-pp-v1.0.3.tar.gz", | |
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| "text": "http://www.speech.sri.com/projects/srilm/ 9 http://homepages.inf.ed.ac.uk/jschroe1/howto/scripts.tgz 10 http://sourceforge.net/projects/mecab/files/ 11 http://sourceforge.net/projects/mecab/files/mecabipadic/", | |
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| "text": "Code available at http://www.kecl.ntt.co.jp/icl/lirg/ribes", | |
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| "FIGREF0": { | |
| "uris": null, | |
| "type_str": "figure", | |
| "num": null, | |
| "text": "Illustration of a word-aligned HPSG-tree-to-string pair for English-to-Japanese translation." | |
| }, | |
| "FIGREF1": { | |
| "uris": null, | |
| "type_str": "figure", | |
| "num": null, | |
| "text": "Number (natural log) of the types of the PASs that appeared in the experiment sets." | |
| }, | |
| "TABREF2": { | |
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| "content": "<table><tr><td>: PAS-based pre-ordering rules extracted</td></tr><tr><td>from the example shown in Figure 1. We use real</td></tr><tr><td>words instead of predicate nodes here for intuitive</td></tr><tr><td>understanding.</td></tr><tr><td>Algorithm 2 Pre-ordering Rule Application</td></tr><tr><td>1: TE.localize() 2: TE.computeSrcSpans() 3: mct rule \u2190 {} 4: for each leaf node t of TE do 5: Node[] args \u2190 TE.getArgs(t) 6: int[] srcOrder \u2190 SORTSPANS(t.srcSpan, srcSpans of args) 7: Rule r \u2190RULEMATCH(R, < t, args, srcOrder>) 8: if r != NULL then 9: mct \u2190 TE.MCT(t, args) 10: mct rule.add(<mct, r >) 11: end if 12: end for 13: for each mct in mct rule in a bottom-up order do 14: Rule r \u2190 mct rule.get(mct) 15: mct.root().srcPhrase \u2190 '' \u25c3 root() returns root node 16: for i from 0 to r.trgOrder.length-1 do 17: mct.root().srcPhrase += ' ' + mct.leaves() [r.trgOrder[i]].srcPhrase 18: end for 19: end for 20: for each node n in TE in a topological order do 21: if n is a terminal node then 22: n.srcPhrase \u2190 E[n.srcSpan[0]] 23: else if n.srcPhrase = NULL then 24: n.srcPhrase \u2190 CONNECT(n.children().srcPhrase) 25: end if 26: end for</td></tr></table>" | |
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| "TABREF6": { | |
| "num": null, | |
| "text": "Statistics of the experiment sets.", | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table/>" | |
| }, | |
| "TABREF8": { | |
| "num": null, | |
| "text": "Statistics of the number of arguments of the predicate words in the experiment sets.", | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table><tr><td>Number</td><td>Ratio</td></tr><tr><td colspan=\"2\">Parse success 45,617,387 94.4% Opened 35,004,893 76.7% Aligned 33,966,923 97.0% Contiguous 30,256,858 89.1%</td></tr></table>" | |
| }, | |
| "TABREF9": { | |
| "num": null, | |
| "text": "Statistics of predicate words in the training set for rule extraction.", | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table/>" | |
| }, | |
| "TABREF11": { | |
| "num": null, | |
| "text": "", | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table><tr><td>, also decreases. The number</td></tr><tr><td>of reordering rules occurs from 25.1% (PAS-d) to</td></tr><tr><td>38.2% (PAS-a) in the pre-ordering rules. For each</td></tr><tr><td>English sentence in the training set, there are aver-</td></tr><tr><td>agely 12 reordering rules (instead of monotonic</td></tr></table>" | |
| }, | |
| "TABREF12": { | |
| "num": null, | |
| "text": "", | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table><tr><td>: Translation accuracies by using the orig-</td></tr><tr><td>inal English sentences or the pre-ordered English</td></tr><tr><td>sentences under four types of pre-ordering rules.</td></tr><tr><td>pre-ordering rules) available under either of the</td></tr><tr><td>four feature subsets. For each English sentence in</td></tr><tr><td>dev.a and dev.b, the number of available reorder-</td></tr><tr><td>ing rules is averagely 16. Around 99.1%, 99.0%,</td></tr><tr><td>and 98.6% English sentences were respectively re-</td></tr><tr><td>ordered in the training set, dev.a set, and dev.b set.</td></tr></table>" | |
| }, | |
| "TABREF13": { | |
| "num": null, | |
| "text": ".7379 11.0% 34.7% HFE+PAS-b 0.3302 0.7397 12.3% 32.8% HFE+PAS-c 0.3300 0.7380 10.8% 35.0% HFE+PAS-d 0.3256 0.7337 11.5% 32.8%", | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table><tr><td colspan=\"3\">Source sent. BLEU RIBES Same</td><td>PAS</td></tr><tr><td>HFE HFE+PAS-a</td><td>0.3134 0.7370 0.3278 0</td><td>-</td><td>-</td></tr></table>" | |
| }, | |
| "TABREF15": { | |
| "num": null, | |
| "text": "Comparison of Kendall's \u03c4 .", | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table/>" | |
| } | |
| } | |
| } | |
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