Maximum entropy based phrase reordering model for statistical machine translation

Xiong, Deyi; Liu, Qun; Lin, Sheng

doi:10.3115/1220175.1220241

Cited by 129 publications

(135 citation statements)

References 15 publications

Supporting

Mentioning

129

Contrasting

Unclassified

Order By: Relevance

“…However, the original BTG does not provide an effective mechanism to predict the most appropriate orders between two neighboring phrases. To address this problem, Xiong et al (2006) enhance the BTG with a maximum entropy (MaxEnt) based reordering model which uses boundary words of bilingual phrases as features. Although this model outperforms previous unlexicalized models, it does not utilize any linguistically syntactic features, which have proven useful for phrase reordering (Wang et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

A linguistically annotated reordering model for BTG-based statistical machine translation

Xiong

Zhang

et al. 2008

Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies Short Pa

Self Cite

View full text Add to dashboard Cite

In this paper, we propose a linguistically annotated reordering model for BTG-based statistical machine translation. The model incorporates linguistic knowledge to predict orders for both syntactic and non-syntactic phrases. The linguistic knowledge is automatically learned from source-side parse trees through an annotation algorithm. We empirically demonstrate that the proposed model leads to a significant improvement of 1.55% in the BLEU score over the baseline reordering model on the NIST MT-05 Chinese-to-English translation task.

show abstract

Section: Introductionmentioning

confidence: 99%

A linguistically annotated reordering model for BTG-based statistical machine translation

Xiong

Zhang

et al. 2008

Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies Short Pa

Self Cite

View full text Add to dashboard Cite

show abstract

“…In hierarchical phrase-based SMT, classifiers have been used to disambiguate between translations of words and short phrases (Chan et al, 2007), and to model reordering decisions (Xiong et al, 2006). In contrast, our implementation lets us directly score each translation rule, and can simultaneously be used to model soft syntactic constraints and lexical disambiguation clues.…”

Section: Discussionmentioning

confidence: 99%

Integrating a Discriminative Classifier into Phrase-based and Hierarchical Decoding

Tamchyna¹,

Braune²,

Fraser³

et al. 2014

The Prague Bulletin of Mathematical Linguistics

View full text Add to dashboard Cite

Current state-of-the-art statistical machine translation (SMT) relies on simple feature functions which make independence assumptions at the level of phrases or hierarchical rules. However, it is well-known that discriminative models can benefit from rich features extracted from the source sentence context outside of the applied phrase or hierarchical rule, which is available at decoding time. We present a framework for the open-source decoder Moses that allows discriminative models over source context to easily be trained on a large number of examples and then be included as feature functions in decoding.

show abstract

“…To better deal with the data sparseness, Zens and Ney [57] introduced more features, i.e., words, word classes, local context, into their discriminative reordering model, and used the framework of maximum entropy to handle all different features. Differently, Xiong et al [58] used the idea of maximum entropy to build a classification model since they only considered types of reorderings, straight and inverted. A recent work on lexicalized reordering model was presented by [59] in which the authors trained a syntactic analogue of a lexicalized reordering model by using multiword syntactic labels from Combinatory categorial grammar parse charts for Urdu-to-English translation.…”

Section: Language Independent Reorderingmentioning

confidence: 99%

Syntax-Based Pre-reordering for Chinese-to-Japanese Statistical Machine Translation

Han

Martínez-Gómez

Miyao

2016

Hybrid Approaches to Machine Translation

View full text Add to dashboard Cite

Bilingual phrases are the main building blocks in statistical machine translation (SMT) systems. At training time, the most likely word-to-word alignment is computed and several heuristics are used to extract these bilingual phrases. Although this strategy performs relatively well when the source and target languages have a similar word order, the quality of extracted bilingual phrases diminishes when translating between languages structurally different, such as Chinese and Japanese. Syntax-based reordering methods in preprocessing stage have been developed and proved to be useful to aid the extraction of bilingual phrases and decoding. For Chinese-to-Japanese SMT, we carry out a detailed linguistic analysis on word order differences of this language pair to improve the word alignment. Our main contribution is threefold: (1) We first adapt an existing pre-reordering method called Head-finalization (HF) [1] for Chinese (HFC) [2] to improve Chinese-to-Japanese SMT system's translation quality. HF is originally designed to reorder English sentences for English-to-Japanese SMT and it performs well. However, our preliminary experiments results reveal its disadvantages on reordering Chinese due to particular characteristics of languages. We thus refine HF to HFC based on a deep linguistic study. To obtain the required syntactic information, we use a head-driven phrase structure grammar (HPSG) parser for Chinese. Nevertheless, the follow-up error analysis from the pre-reordering experiment explores more issues that bring difficulties for further improvement on HFC, such as the tree operation restriction of binary tree, inconsistency on definition of linguistic term and so on. (2) We then propose an entire new pre-reordering framework which is using an unlabeled dependency parser to achieve additional improvements on reordering Chinese sentences to be like Japanese word orders.We refer to it as DPC [3] for short. In this method, we first identify blocks of Chinese words that demand reorderings, such as verbs and certain particles. Then, we detect the proper position which is the right-hand side of their rightmost object dependent, since our reordering principle is to reorder a Subject-Verb-Object (SVO) language to resemble a Subject-Object-Verb (SOV) language. Other types of particles are relocated in the last step. Unlike other reordering systems, the boundaries of verbal blocks and their rightmost object in DPC are defined only by the dependency tree and part-of-speech tags.Additionally, dismissing of using structural and punctuation border is another benefit for the reordering of the reported speech frequently occurring in news domain. The experiments show advantages of DPC over the SMT baseline (Moses) and our HFC systems.Important advantages of this method are the applicability of many reordering rules to other SVO and SOV language pairs as well as the availability of dependency parsers and POS-taggers for many languages. Considering our pre-reordering methods of HFC and DPC are linguistically-motivated,...

show abstract

Maximum entropy based phrase reordering model for statistical machine translation

Cited by 129 publications

References 15 publications

A linguistically annotated reordering model for BTG-based statistical machine translation

A linguistically annotated reordering model for BTG-based statistical machine translation

Integrating a Discriminative Classifier into Phrase-based and Hierarchical Decoding

Syntax-Based Pre-reordering for Chinese-to-Japanese Statistical Machine Translation

Contact Info

Product

Resources

About