Using a dependency parser to improve SMT for subject-object-verb languages

Xu, Peng; Kang, Jaeho; Ringgaard, Michael; Och, Franz Josef

doi:10.3115/1620754.1620790

Cited by 53 publications

(57 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This contrasts with HFC that requires phrase structures, phrase-head information and POS tags, and the work in [63] that requires dependency relations, dependency labels and POS tags.…”

Section: Discussionmentioning

confidence: 99%

“…Linguistically motivated pre-reordering method usually involves a parser/parsers via which to obtain syntactic information of either source/target language or both, and this method has been proved to be an efficient auxiliary technique for a traditional phrase-based SMT system to improve the translation quality by many researches [60][61][62][63][64][65], especially when source and target languages are structurally very different, such as German-English [61], English-Arabic [66], English-Hindi [67], Japanese-English [1,68], English to multiple SOV/VSO languages (i.e., Korean, Japanese, Hindi, Urdu, Welsh and Turkish) or noun-modifier issues (i.e., Russian and Czech) [63,64] and so on. As…”

Section: Language Dependent Reorderingmentioning

confidence: 99%

“…One is to create handcrafted reordering rules based on linguistic analysis [61][62][63]68], whereas another one is to learn reordering rules from the data [60,64,65,71,72]. Xia and McCord [60] presented a method to automatically learn rewriting patterns from the combination of aligned phrases and their parse tree pairs.…”

Section: Language Dependent Reorderingmentioning

confidence: 99%

“…We hence propose another new pre-reordering framework for Chinese based on unlabeled dependency parsing (DPC). One similar work as ours was introduced in [63], which was using an English dependency parser to formulate handcrafted reordering rules in the form of triplet that is composed of dependency labels, part-of-speech (POS) tags and weights. The rules were operated recursively in a sentence while reordering.…”

Section: Language Dependent Reorderingmentioning

confidence: 99%

See 3 more Smart Citations

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

“…This contrasts with HFC that requires phrase structures, phrase-head information and POS tags, and the work in [63] that requires dependency relations, dependency labels and POS tags.…”

Section: Discussionmentioning

confidence: 99%

Section: Language Dependent Reorderingmentioning

confidence: 99%

Section: Language Dependent Reorderingmentioning

confidence: 99%

Section: Language Dependent Reorderingmentioning

confidence: 99%

See 2 more Smart Citations

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

show abstract

“…Examples of these can be found for French-English (Xia and McCord, 2004), German-English (Collins et al, 2005), ChineseEnglish (Wang et al, 2007), English-Arabic (Badr et al, 2009), English-Hindi (Ramanathan et al, 2009), English-Korean (Hong et al, 2009), and English-Japanese (Lee et al, 2010;Isozaki et al, 2010). A generic set of rules for transforming SVO to SOV languages has also been described (Xu et al, 2009). The main advantage of these approaches is that a relatively small set of good rules can yield significant improvements in translation.…”

Section: Related Workmentioning

confidence: 99%

Source-side Preordering for Translation using Logistic Regression and Depth-first Branch-and-Bound Search

Jehl

Gispert²,

Hopkins³

et al. 2014

Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

View full text Add to dashboard Cite

We present a simple preordering approach for machine translation based on a featurerich 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 children regression scores we conduct an efficient depth-first branch-and-bound search through the space of possible children permutations, avoiding using a cascade of classifiers or limiting the list of possible ordering outcomes. We report experiments in translating English to Japanese and Korean, demonstrating superior performance as (a) the number of crossing links drops by more than 10% absolute with respect to other state-of-the-art preordering approaches, (b) BLEU scores improve on 2.2 points over the baseline with lexicalised reordering model, and (c) decoding can be carried out 80 times faster.

show abstract

A Neural Network Classifier Based on Dependency Tree for English-Vietnamese Statistical Machine Translation

Tran¹,

Nguyen²,

Nguyen³

2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Using a dependency parser to improve SMT for subject-object-verb languages

Cited by 53 publications

References 23 publications

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

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

Source-side Preordering for Translation using Logistic Regression and Depth-first Branch-and-Bound Search

A Neural Network Classifier Based on Dependency Tree for English-Vietnamese Statistical Machine Translation

Contact Info

Product

Resources

About