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23
A topic similarity model for hierarchical phrase-based translation
- In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers
, 2012
"... Abstract Previous work using topic model for statistical machine translation (SMT) explore topic information at the word level. However, SMT has been advanced from word-based paradigm to phrase/rule-based paradigm. We therefore propose a topic similarity model to exploit topic information at the sy ..."
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Abstract Previous work using topic model for statistical machine translation (SMT) explore topic information at the word level. However, SMT has been advanced from word-based paradigm to phrase/rule-based paradigm. We therefore propose a topic similarity model to exploit topic information at the synchronous rule level for hierarchical phrase-based translation. We associate each synchronous rule with a topic distribution, and select desirable rules according to the similarity of their topic distributions with given documents. We show that our model significantly improves the translation performance over the baseline on NIST Chinese-to-English translation experiments. Our model also achieves a better performance and a faster speed than previous approaches that work at the word level.
Improved Translation with Source Syntax Labels
"... We present a new translation model that include undecorated hierarchical-style phrase rules, decorated source-syntax rules, and partially decorated rules. Results show an increase in translation performance of up to 0.8 % BLEU for German–English translation when trained on the news-commentary corpus ..."
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Cited by 9 (1 self)
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We present a new translation model that include undecorated hierarchical-style phrase rules, decorated source-syntax rules, and partially decorated rules. Results show an increase in translation performance of up to 0.8 % BLEU for German–English translation when trained on the news-commentary corpus, using syntactic annotation from a source language parser. We also experimented with annotation from shallow taggers and found this increased performance by 0.5 % BLEU. 1
Maximum Entropy Based Phrase Reordering for Hierarchical Phrase-based Translation
"... Hierarchical phrase-based (HPB) translation provides a powerful mechanism to capture both short and long distance phrase reorderings. However, the phrase reorderings lack of contextual information in conventional HPB systems. This paper proposes a contextdependent phrase reordering approach that use ..."
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Cited by 8 (0 self)
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Hierarchical phrase-based (HPB) translation provides a powerful mechanism to capture both short and long distance phrase reorderings. However, the phrase reorderings lack of contextual information in conventional HPB systems. This paper proposes a contextdependent phrase reordering approach that uses the maximum entropy (MaxEnt) model to help the HPB decoder select appropriate reordering patterns. We classify translation rules into several reordering patterns, and build a MaxEnt model for each pattern based on various contextual features. We integrate the MaxEnt models into the HPB model. Experimental results show that our approach achieves significant improvements over a standard HPB system on large-scale translation tasks. On Chinese-to-English translation, the absolute improvements in BLEU (caseinsensitive) range from 1.2 to 2.1. 1
Using Syntactic Head Information in Hierarchical Phrase-Based Translation
"... Chiang’s hierarchical phrase-based (HPB) translation model advances the state-of-the-art in statistical machine translation by expanding conventional phrases to hierarchical phrases – phrases that contain sub-phrases. However, the original HPB model is prone to overgeneration due to lack of linguist ..."
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Chiang’s hierarchical phrase-based (HPB) translation model advances the state-of-the-art in statistical machine translation by expanding conventional phrases to hierarchical phrases – phrases that contain sub-phrases. However, the original HPB model is prone to overgeneration due to lack of linguistic knowledge: the grammar may suggest more derivations than appropriate, many of which may lead to ungrammatical translations. On the other hand, limitations of glue grammar rules in the original HPB model may actually prevent systems from considering some reasonable derivations. This paper presents a simple but effective translation model, called the Head-Driven HPB (HD-HPB) model, which incorporates head information in translation rules to better capture syntax-driven information in a derivation. In addition, unlike the original glue rules, the HD-HPB model allows improved reordering between any two neighboring non-terminals to explore a larger reordering search space. An extensive set of experiments on Chinese-English translation on four NIST MT test sets, using both a small and a large training set, show that our HD-HPB model consistently and statistically significantly outperforms Chiang’s model as well as a source side SAMT-style model. 1
Head-Driven Hierarchical Phrase-based Translation
"... This paper presents an extension of Chiang’s hierarchical phrase-based (HPB) model, called Head-Driven HPB (HD-HPB), which incorporates head information in translation rules to better capture syntax-driven information, as well as improved reordering between any two neighboring non-terminals at any s ..."
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This paper presents an extension of Chiang’s hierarchical phrase-based (HPB) model, called Head-Driven HPB (HD-HPB), which incorporates head information in translation rules to better capture syntax-driven information, as well as improved reordering between any two neighboring non-terminals at any stage of a derivation to explore a larger reordering search space. Experiments on Chinese-English translation on four NIST MT test sets show that the HD-HPB model significantly outperforms Chiang’s model with average gains of 1.91 points absolute in BLEU. 1
Hierarchical Phrase-based Machine Translation with Word-based Reordering Model
- In Proceedings of the COLING. Zhongjun
, 2010
"... Hierarchical phrase-based machine trans-lation can capture global reordering with synchronous context-free grammar, but has little ability to evaluate the correctness of word orderings during decoding. We propose a method to integrate word-based reordering model into hierarchical phrase-based machin ..."
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Hierarchical phrase-based machine trans-lation can capture global reordering with synchronous context-free grammar, but has little ability to evaluate the correctness of word orderings during decoding. We propose a method to integrate word-based reordering model into hierarchical phrase-based machine translation to overcome this weakness. Our approach extends the synchronous context-free grammar rules of hierarchical phrase-based model to in-clude reordered source strings, allowing efficient calculation of reordering model scores during decoding. Our experimen-tal results on Japanese-to-English basic travel expression corpus showed that the BLEU scores obtained by our proposed system were better than those obtained by a standard hierarchical phrase-based ma-chine translation system. 1
Statistical Machine Translation with Local Language Models
"... Part-of-speech language modeling is commonly used as a component in statistical machine translation systems, but there is mixed evidence that its usage leads to significant improvements. We argue that its limited effectiveness is due to the lack of lexicalization. We introduce a new approach that bu ..."
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Part-of-speech language modeling is commonly used as a component in statistical machine translation systems, but there is mixed evidence that its usage leads to significant improvements. We argue that its limited effectiveness is due to the lack of lexicalization. We introduce a new approach that builds a separate local language model for each word and part-of-speech pair. The resulting models lead to more context-sensitive probability distributions and we also exploit the fact that different local models are used to estimate the language model probability of each word during decoding. Our approach is evaluated for Arabic- and Chinese-to-English translation. We show that it leads to statistically significant improvements for multiple test sets and also across different genres, when compared against a competitive baseline and a system using a part-of-speech model. 1
A Joint Rule Selection Model for Hierarchical Phrase-based Translation ∗
"... In hierarchical phrase-based SMT systems, statistical models are integrated to guide the hierarchical rule selection for better translation performance. Previous work mainly focused on the selection of either the source side of a hierarchical rule or the target side of a hierarchical rule rather tha ..."
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Cited by 3 (0 self)
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In hierarchical phrase-based SMT systems, statistical models are integrated to guide the hierarchical rule selection for better translation performance. Previous work mainly focused on the selection of either the source side of a hierarchical rule or the target side of a hierarchical rule rather than considering both of them simultaneously. This paper presents a joint model to predict the selection of hierarchical rules. The proposed model is estimated based on four sub-models where the rich context knowledge from both source and target sides is leveraged. Our method can be easily incorporated into the practical SMT systems with the log-linear model framework. The experimental results show that our method can yield significant improvements in performance. 1
Two-Neighbor Orientation Model with Cross-Boundary Global Contexts
"... Long distance reordering remains one of the greatest challenges in statistical machine translation research as the key contextual information may well be beyond the confine of translation units. In this paper, we propose Two-Neighbor Orientation (TNO) model that jointly models the orientation decisi ..."
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Cited by 2 (1 self)
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Long distance reordering remains one of the greatest challenges in statistical machine translation research as the key contextual information may well be beyond the confine of translation units. In this paper, we propose Two-Neighbor Orientation (TNO) model that jointly models the orientation decisions between anchors and two neighboring multi-unit chunks which may cross phrase or rule boundaries. We explicitly model the longest span of such chunks, referred to as Maximal Orientation Span, to serve as a global parameter that constrains underlying local decisions. We integrate our proposed model into a state-of-the-art string-to-dependency translation system and demonstrate the efficacy of our proposal in a large-scale Chinese-to-English translation task. On NIST MT08 set, our most advanced model brings around +2.0 BLEU and-1.0 TER improvement.
Supertags as Source Language Context in Hierarchical Phrase-Based SMT
"... Statistical machine translation (SMT) models have recently begun to include source context modeling, under the assumption that the proper lexical choice of the translation for an ambiguous word can be determined from the context in which it appears. Various types of lexical and syntactic features ha ..."
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Statistical machine translation (SMT) models have recently begun to include source context modeling, under the assumption that the proper lexical choice of the translation for an ambiguous word can be determined from the context in which it appears. Various types of lexical and syntactic features have been explored as effective source context to improve phrase selection in SMT. In the present work, we introduce lexico-syntactic descriptions in the form of supertags as source-side context features in the state-of-the-art hierarchical phrase-based SMT (HPB) model. These features enable us to exploit source similarity in addition to target similarity, as modelled by the language model. In our experiments two kinds of supertags are employed: those from lexicalized tree-adjoining grammar (LTAG) and combinatory categorial grammar (CCG). We use a memory-based classification framework that enables the efficient estimation of these features. Despite the differences between the two supertagging approaches, they give similar improvements. We evaluate the performance of our approach on an English-to-Dutch translation task, and report statistically significant improvements of 4.48 % and 6.3 % BLEU scores in translation quality when adding CCG and LTAG supertags, respectively, as context-informed features. 1
