Syntactic Reordering of Source Sentences for Statistical Machine Translation
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1 Syntactic Reordering of Source Sentences for Statistical Machine Translation Mohammad Sadegh Rasooli Columbia University April 9, 2013 M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
2 Overview 1 First Paper: Collins, et al. (2005) The Role of Syntax in SMT Syntactic Preprocessing Approaches Clause Restructing Experiments Discussion 2 Second Paper: P. Xu, et al., (2009). Approaches to Syntactic Reordering Translation Between SVO and SOV Languages Precedence Reordering Based on a Dependency Parser Experiments Discussion M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
3 First Paper M. Collins, et al.: Clause Restructuring for Statistical Machine Translation. ACL M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
4 The Role of Syntax in SMT In the original phrase-base SMT, syntax is not taken into acount. Phrase-based systems have limited potential to model word-order differences between languages. The word order differences between languages are considered as distortion. Each reordering rule adds distortion penalties to the overall score of the translation model. M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
5 Example: German vs. English Word Order English I will pass on to you the corresponding comments, so that you can adopt them perhaps in the vote. German I will to you the corresponding comments pass on, so that you them perhaps in the vote adopt can. M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
6 Research on Syntax in MT Changing the word order of one of the languages or both, to make their word order more similar to each other. Syntax-Based MT Approaches Make use of bitext grammars to parse both parts. Change the syntax of target language alone. Transform the translation problem into a parsing problem. Reranking methods Select between N-best results of the phrase-based system, using syntactic information. Preprocessing Approaches The source language sentences are modified before translation. This approach is used in this paper. M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
7 Syntactic Preprocessing Approaches English I will pass on to you the corresponding comments, so that you can adopt them perhaps in the vote. German I will to you the corresponding comments pass on, so that you them perhaps in the vote adopt can. German (Preprocessed) I will pass on to you the corresponding comments, so that you can adopt them perhaps in the vote. M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
8 Clause Restructing Steps (both in training and decoding) 1 Parse the source sentence. 2 Apply reordering rules on the source sentence. 3 Use phrase-based models. M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
9 Example Parse Tree M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
10 Six Reordering Rules in German M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
11 Experiments Experimental setup Data: Europarl Corpus. 751,088 parallel sentence. Evaluation on 2000 sentences. Average sentence length: 28 words Baseline: no reordering phrase-based system. Results (BLEU score) Basline: 25.2% Reordering: 26.8% M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
12 Human Translation Judgments Two annotators judged 100 sentences (10 to 20 words in length; chosen at random). Three versions: Human, baseline, reordered. Judgments: Worse/better or equal. Better Equal Worse Annotator 1 40% 40% 20% Annotator 2 44% 37% 19% M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
13 Example Output Human i think it is wrong in principle to have such measures in the european union. Reordered i believe that it is wrong in principle to take such measures in the european union. Baseline i believe that it is wrong in principle such measures in the european union to take. M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
14 BLEU Statistical Significance Authors use sign test for statistical significance. f(x) is + if better than baseline, f(x) is - if worse; and f(x) is = if equal p + : probability of (f(x) is +) and p : probability of f(x) is minus BLEU does not have per-sentence evaluation. Authors create an artificial comparison: s baseline BLEU score s i baseline BLEU score except the sentence i translated by the reordered model. f(x) is + is s i > s; f(x) is - is s i < s % improved, 36.4% worse than baseline and 10.75% equal. With 95% confidence, this method improves the baseline. M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
15 Discussion The method clearly improves the baseline. The rules are language-specific (even cannot be used for English to German translation). The authors did not try to learn reordering rules automatically. M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
16 Second Paper P. Xu, et al., Using a dependency parser to improve SMT for subject-object-verb languages. NAACL M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
17 Approaches to Syntactic Reordering Explicitly model phrase reordering distances; e.g. distance based distortion models. Syntactic analysis of the target language into both modeling and decoding. Reordering source sentences based on syntactic analysis This paper uses this approach M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
18 Translation Between SVO and SOV Languages Subject-Verb-Object (SVO) and Subject-Object-Verb (SOV) are two common word order in the world languages. English is SVO and Korean is SOV. John hit the ball. vs. John the ball hit. When the sentences get longer, the cost of moving structures during decoding (in phrase-based models) can be quite high. English is used as the first or second language in many countries around the world. is used should skip 13 words to go to the end of the sentence. M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
19 Precedence Reordering Based on a Dependency Parser The children of each word have some relative ordering. A Precedence reordering rule is a mapping from T to a set of tuples {(L, W, O)} T : POS tag L: Dependency label W : Weight indicating the order (highest to lowest) Children with the same weights are ordered according to the order defined in the rule. Why not explicitly pre-define unequal weights? O: order type NORMAL: preserve the original order RESERVE: flip the order If a node is not listed in the rules, W = 0 and O = NORMAL Use self to refer to the head node itself. Punctuations and conjugations disallow movements across them. M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
20 Precedence Reordering Based on a Dependency Parser After apply precedence rule, this will be: John the ball hit can. M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
21 Novelties in This Work 1 This model is more efficient than its counterpart. 2 Outperforms the state-of-the-art (stronger baseline). 3 It is not restricted to one language pair. 4 It is possible to automatically learn precedence rules. 5 They use dependency parse trees rather than constituency trees. M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
22 Experiments English to 5 SOV languages. Baseline: Maximum entropy based lexicalized phrase reordering model. Maximum allowed reordering: 10. Parser: Deterministic transition-based dependency parser. Parses in linear time. Another baseline: Hierarchical phrase-based system. Can capture long distance reordering by using a PCFG model. Uses chart parsing during decoding: slower than deterministic dependency parser. 9.5K English sentences (from web) as evaluation data. 3,500 sentences for dev (to perform MERT). 1,000 sentences for test. 5,000 sentences for blind test. M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
23 Experiments M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
24 Results M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
25 Results M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
26 Discussion Reordering of languages with different word orders is essential. The method seems to work fine for 5 languages. Although authors claim that the rule can be extracted automatically, they did not try. The improvement of the basic over hierarchical phrase-based is not significant. M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
27 Thanks! M. S. Rasooli (Columbia University) Syntactic Reordering for SMT April 9, / 27
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