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Modeling the translation of predicate-argument structure for smt
- In ACL
, 2012
"... Predicate-argument structure contains rich semantic information of which statistical machine translation hasn’t taken full advantage. In this paper, we propose two discriminative, feature-based models to exploit predicateargument structures for statistical machine translation: 1) a predicate transla ..."
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Cited by 11 (0 self)
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Predicate-argument structure contains rich semantic information of which statistical machine translation hasn’t taken full advantage. In this paper, we propose two discriminative, feature-based models to exploit predicateargument structures for statistical machine translation: 1) a predicate translation model and 2) an argument reordering model. The predicate translation model explores lexical and semantic contexts surrounding a verbal predicate to select desirable translations for the predicate. The argument reordering model automatically predicts the moving direction of an argument relative to its predicate after translation using semantic features. The two models are integrated into a state-of-theart phrase-based machine translation system and evaluated on Chinese-to-English translation tasks with large-scale training data. Experimental results demonstrate that the two models significantly improve translation accuracy. 1
Head finalization reordering for Chinese-to-Japanese machine translation.” in
- Proc. of SSST,
, 2012
"... Abstract In Statistical Machine Translation, reordering rules have proved useful in extracting bilingual phrases and in decoding during translation between languages that are structurally different. Linguistically motivated rules have been incorporated into Chineseto-English ..."
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Cited by 4 (1 self)
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Abstract In Statistical Machine Translation, reordering rules have proved useful in extracting bilingual phrases and in decoding during translation between languages that are structurally different. Linguistically motivated rules have been incorporated into Chineseto-English
Handling Ambiguities of Bilingual Predicate-Argument Structures for Statistical Machine Translation
"... Predicate-argument structure (PAS) has been demonstrated to be very effective in improving SMT performance. However, since a sourceside PAS might correspond to multiple different target-side PASs, there usually exist many PAS ambiguities during translation. In this paper, we group PAS ambiguities in ..."
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Cited by 2 (0 self)
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Predicate-argument structure (PAS) has been demonstrated to be very effective in improving SMT performance. However, since a sourceside PAS might correspond to multiple different target-side PASs, there usually exist many PAS ambiguities during translation. In this paper, we group PAS ambiguities into two types: role ambiguity and gap ambiguity. Then we propose two novel methods to handle the two PAS ambiguities for SMT accordingly: 1) inside context integration; 2) a novel maximum entropy PAS disambiguation (MEPD) model. In this way, we incorporate rich context information of PAS for disambiguation. Then we integrate the two methods into a PASbased translation framework. Experiments show that our approach helps to achieve significant improvements on translation quality. 1
Machine Translation by Modeling PredicateArgument Structure Transformation
"... Machine translation aims to generate a target sentence that is semantically equivalent to the source sentence. However, most of current statistical machine translation models do not model the semantics of sentences. In this paper, we propose a novel translation framework based on predicate-argument ..."
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Cited by 1 (0 self)
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Machine translation aims to generate a target sentence that is semantically equivalent to the source sentence. However, most of current statistical machine translation models do not model the semantics of sentences. In this paper, we propose a novel translation framework based on predicate-argument structure (PAS) for its capacity on grasping the semantics and skeleton structure of sentences. By using PAS, the framework effectively models both semantics of languages and global reordering for translation. In the framework, we divide the translation process into 3 steps: (1) PAS acquisition: perform semantic role labeling (SRL) on the input sentences to acquire source-side PASs; (2) Transformation: convert source-side PASs to their target counterparts by predicate-aware PAS transformation rules; (3) Translation: first translate the predicate and arguments of PAS and then adopt a CKY-style decoding algorithm to translate the entire PAS. Experimental results show that our PAS-based translation framework significantly improves the translation performance.
Using unlabeled dependency parsing for prereordering for chinese-to-japanese statistical machine translation
- In ACL Workshop on Hybrid Machine Approaches to Translation
, 2013
"... Chinese and Japanese have a different sentence structure. Reordering methods are effective, but need reliable parsers to extract the syntactic structure of the source sentences. However, Chinese has a loose word order, and Chinese parsers that extract the phrase structure do not perform well. We pro ..."
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Chinese and Japanese have a different sentence structure. Reordering methods are effective, but need reliable parsers to extract the syntactic structure of the source sentences. However, Chinese has a loose word order, and Chinese parsers that extract the phrase structure do not perform well. We propose a framework where only POS tags and unlabeled dependency parse trees are necessary, and linguistic knowledge on structural difference can be encoded in the form of reordering rules. We show significant improvements in translation quality of sentences from news domain, when compared to state-of-the-art reordering methods. 1
A Unified Model for Soft Linguistic Reordering Constraints in Statistical Machine Translation
"... Abstract This paper explores a simple and effective unified framework for incorporating soft linguistic reordering constraints into a hierarchical phrase-based translation system: 1) a syntactic reordering model that explores reorderings for context free grammar rules; and 2) a semantic reordering ..."
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Abstract This paper explores a simple and effective unified framework for incorporating soft linguistic reordering constraints into a hierarchical phrase-based translation system: 1) a syntactic reordering model that explores reorderings for context free grammar rules; and 2) a semantic reordering model that focuses on the reordering of predicate-argument structures. We develop novel features based on both models and use them as soft constraints to guide the translation process. Experiments on Chinese-English translation show that the reordering approach can significantly improve a state-of-the-art hierarchical phrase-based translation system. However, the gain achieved by the semantic reordering model is limited in the presence of the syntactic reordering model, and we therefore provide a detailed analysis of the behavior differences between the two.
Source-side classifier . . .
, 2013
"... We present a simple and novel classifier-based preordering approach. Unlike existing preordering models, we train feature-rich discriminative classifiers that directly predict the target-side word order. Our approach combines the strengths of lexical reordering and syntactic preordering models by pe ..."
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We present a simple and novel classifier-based preordering approach. Unlike existing preordering models, we train feature-rich discriminative classifiers that directly predict the target-side word order. Our approach combines the strengths of lexical reordering and syntactic preordering models by performing long-distance reorderings using the structure of the parse tree, while utilizing a discriminative model with a rich set of features, including lexical features. We present extensive experiments on 22 language pairs, including preordering into English from 7 other languages. We obtain improvements of up to 1.4 BLEU on language pairs in the WMT 2010 shared task. For languages from different families the improvements often exceed 2 BLEU. Many of these gains are also significant in human evaluations.
Dependency-based Pre-ordering for Chinese-English Machine Translation
"... In statistical machine translation (SMT), syntax-based pre-ordering of the source language is an effective method for deal-ing with language pairs where there are great differences in their respective word orders. This paper introduces a novel pre-ordering approach based on depen-dency parsing for C ..."
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In statistical machine translation (SMT), syntax-based pre-ordering of the source language is an effective method for deal-ing with language pairs where there are great differences in their respective word orders. This paper introduces a novel pre-ordering approach based on depen-dency parsing for Chinese-English SMT. We present a set of dependency-based pre-ordering rules which improved the BLEU score by 1.61 on the NIST 2006 evalua-tion data. We also investigate the accuracy of the rule set by conducting human eval-uations. 1
Source-side Preordering for Translation using Logistic Regression and Depth-first Branch-and-Bound Search∗
"... We present a simple preordering approach for machine translation based on a feature-rich logistic regression model to predict whether two children of the same node in the source-side parse tree should be swapped or not. Given the pair-wise chil-dren regression scores we conduct an effi-cient depth-f ..."
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We present a simple preordering approach for machine translation based on a feature-rich logistic regression model to predict whether two children of the same node in the source-side parse tree should be swapped or not. Given the pair-wise chil-dren regression scores we conduct an effi-cient depth-first branch-and-bound search through the space of possible children per-mutations, avoiding using a cascade of classifiers or limiting the list of possi-ble ordering outcomes. We report exper-iments in translating English to Japanese and Korean, demonstrating superior per-formance as (a) the number of crossing links drops by more than 10 % absolute with respect to other state-of-the-art pre-ordering approaches, (b) BLEU scores im-prove on 2.2 points over the baseline with lexicalised reordering model, and (c) de-coding can be carried out 80 times faster. 1
