Syntactic phrase-based statistical machine translation

Phrase-based statistical machine translation (PBSMT) systems represent the dominant approach in MT today. However, unlike systems in other paradigms, it has proven difficult to date to incorporate syntactic knowledge in order to improve translation quality. This paper improves on recent research which uses 'syntactified' target language phrases, by incorporating supertags as constraints to better resolve parse tree fragments. In addition, we do not impose any sentence-length limit, and using a log-linear decoder, we outperform a state-of-the-art PBSMT system by over 1.3 BLEU points (or 3.51% relative) on the NIST 2003 Arabic-English test corpus

A detailed analysis of phrase-based and syntax-based machine translation: the search for systematic differences

This paper describes a range of automatic and manual comparisons of phrase-based and syntax-based statistical machine translation methods applied to English-German and English-French translation of user-generated content. The syntax-based methods underperform the phrase-based models and the relaxation of syntactic constraints to broaden translation rule coverage means that these models do not necessarily generate output which is more grammatical than the output produced by the phrase-based models. Although the systems generate different output and can potentially be fruitfully combined, the lack of systematic difference between these models makes the combination task more challenging

Comparing constituency and dependency representations for SMT phrase-extraction

We consider the value of replacing and/or combining string-based methods with syntax-based methods for phrase-based statistical machine translation (PBSMT), and we also consider the relative merits of using constituency-annotated vs. dependency-annotated training data. We automatically derive two subtree-aligned treebanks, dependency-based and constituency-based, from a parallel English–French corpus and extract syntactically motivated word- and phrase-pairs. We automatically measure PB-SMT quality. The results show that combining string-based and syntax-based word- and phrase-pairs can improve translation quality irrespective of the type of syntactic annotation. Furthermore, using dependency annotation yields greater translation quality than constituency annotation for PB-SMT

Sentence similarity-based source context modelling in PBSMT

Target phrase selection, a crucial component of the state-of-the-art phrase-based statistical machine translation (PBSMT) model, plays a key role in generating accurate translation hypotheses. Inspired by context-rich word-sense disambiguation techniques, machine translation (MT) researchers have successfully integrated various types of source language context into the PBSMT model to improve target phrase selection. Among the various types of lexical and syntactic features, lexical syntactic descriptions in the form of supertags that preserve long-range word-to-word dependencies in a sentence have proven to be effective. These rich contextual features are able to disambiguate a source phrase, on the basis of the local syntactic behaviour of that phrase. In addition to local contextual information, global contextual information such as the grammatical structure of a sentence, sentence length and n-gram word sequences could provide additional important information to enhance this phrase-sense disambiguation. In this work, we explore various sentence similarity features by measuring similarity between a source sentence to be translated with the source-side of the bilingual training sentences and integrate them directly into the PBSMT model. We performed experiments on an English-to-Chinese translation task by applying sentence-similarity features both individually, and collaboratively with supertag-based features. We evaluate the performance of our approach and report a statistically significant relative improvement of 5.25% BLEU score when adding a sentence-similarity feature together with a supertag-based feature

Dependency relations as source context in phrase-based SMT

The Phrase-Based Statistical Machine Translation (PB-SMT) model has recently begun to include source context modeling, under the assumption that the proper lexical choice of an ambiguous word can be determined from the context in which it appears. Various types of lexical and syntactic features such as words, parts-of-speech, and supertags have been explored as effective source context in SMT. In this paper, we show that position-independent syntactic dependency relations of the head of a source phrase can be modeled as useful source context to improve target phrase selection and thereby improve overall performance of PB-SMT. On a Dutch—English translation task, by combining dependency relations and syntactic contextual features (part-of-speech), we achieved a 1.0 BLEU (Papineni et al., 2002) point improvement (3.1% relative) over the baseline

Linguistic Structure in Statistical Machine Translation

This thesis investigates the influence of linguistic structure in statistical machine translation. We develop a word reordering model based on syntactic parse trees and address the issues of pronouns and morphological agreement with a source discriminative word lexicon predicting the translation for individual words using structural features. When used in phrase-based machine translation, the models improve the translation for language pairs with different word order and morphological variation

HMM word-to-phrase alignment with dependency constraints

In this paper, we extend the HMMwordto-phrase alignment model with syntactic dependency constraints. The syntactic dependencies between multiple words in one language are introduced into the model in a bid to produce coherent alignments. Our experimental results on a variety of Chinese–English data show that our syntactically constrained model can lead to as much as a 3.24% relative improvement in BLEU score over current HMM word-to-phrase alignment models on a Phrase-Based Statistical Machine Translation system when the training data is small, and a comparable performance compared to IBM model 4 on a Hiero-style system with larger training data. An intrinsic alignment quality evaluation shows that our alignment model with dependency constraints leads to improvements in both precision (by 1.74% relative) and recall (by 1.75% relative) over the model without dependency information

CCG-augmented hierarchical phrase-based statistical machine translation

Augmenting Statistical Machine Translation (SMT) systems with syntactic information aims at improving translation quality. Hierarchical Phrase-Based (HPB) SMT takes a step toward incorporating syntax in Phrase-Based (PB) SMT by modelling one aspect of language syntax, namely the hierarchical structure of phrases. Syntax Augmented Machine Translation (SAMT) further incorporates syntactic information extracted using context free phrase structure grammar (CF-PSG) in the HPB SMT model. One of the main challenges facing CF-PSG-based augmentation approaches for SMT systems emerges from the difference in the definition of the constituent in CF-PSG and the ‘phrase’ in SMT systems, which hinders the ability of CF-PSG to express the syntactic function of many SMT phrases. Although the SAMT approach to solving this problem using ‘CCG-like’ operators to combine constituent labels improves syntactic constraint coverage, it significantly increases their sparsity, which restricts translation and negatively affects its quality. In this thesis, we address the problems of sparsity and limited coverage of syntactic constraints facing the CF-PSG-based syntax augmentation approaches for HPB SMT using Combinatory Cateogiral Grammar (CCG). We demonstrate that CCG’s flexible structures and rich syntactic descriptors help to extract richer, more expressive and less sparse syntactic constraints with better coverage than CF-PSG, which enables our CCG-augmented HPB system to outperform the SAMT system. We also try to soften the syntactic constraints imposed by CCG category nonterminal labels by extracting less fine-grained CCG-based labels. We demonstrate that CCG label simplification helps to significantly improve the performance of our CCG category HPB system. Finally, we identify the factors which limit the coverage of the syntactic constraints in our CCG-augmented HPB model. We then try to tackle these factors by extending the definition of the nonterminal label to be composed of a sequence of CCG categories and augmenting the glue grammar with CCG combinatory rules. We demonstrate that our extension approaches help to significantly increase the scope of the syntactic constraints applied in our CCG-augmented HPB model and achieve significant improvements over the HPB SMT baseline

Using percolated dependencies for phrase extraction in SMT

Statistical Machine Translation (SMT) systems rely heavily on the quality of the phrase pairs induced from large amounts of training data. Apart from the widely used method of heuristic learning of n-gram phrase translations from word alignments, there are numerous methods for extracting these phrase pairs. One such class of approaches uses translation information encoded in parallel treebanks to extract phrase pairs. Work to date has demonstrated the usefulness of translation models induced from both constituency structure trees and dependency structure trees. Both syntactic annotations rely on the existence of natural language parsers for both the source and target languages. We depart from the norm by directly obtaining dependency parses from constituency structures using head percolation tables. The paper investigates the use of aligned chunks induced from percolated dependencies in French–English SMT and contrasts it with the aforementioned extracted phrases. We observe that adding phrase pairs from any other method improves translation performance over the baseline n-gram-based system, percolated dependencies are a good substitute for parsed dependencies, and that supplementing with our novel head percolation-induced chunks shows a general trend toward improving all system types across two data sets up to a 5.26% relative increase in BLEU

Using supertags as source language context in SMT

Recent research has shown that Phrase-Based Statistical Machine Translation (PB-SMT) systems can benefit from two enhancements: (i) using words and POS tags as context-informed features on the source side; and (ii) incorporating lexical syntactic descriptions in the form of supertags on the target side. In this work we present a novel PB-SMT model that combines these two aspects by using supertags as source language contextinformed features. 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 and Combinatory Categorial Grammar. We use a memory-based classification framework that enables the estimation of these features while avoiding problems of sparseness. Despite the differences between these two approaches, the supertaggers give similar improvements. We evaluate the performance of our approach on an English-to-Chinese translation task using a state-of-the-art phrase-based SMT system, and report an improvement of 7.88% BLEU score in translation quality when adding supertags as context-informed features