Discriminative Reordering Extensions for Hierarchical Phrase-Based Machine Translation (original) (raw)

A Model Lexicalized Hierarchical Reordering for Phrase Based Translation

Procedia - Social and Behavioral Sciences, 2011

In this paper, we present a reordering model based on Maximum Entropy with local and non-local features. This model is extended from a hierarchical reordering model with PBSMT [1], which integrates rich syntactic information directly in decoder as local and non-local features of Maximum Entropy model. The advantages of this model are (1) maintaining the strength of phrase based approach with a hierarchical reordering model, (2) many kinds of rich linguistic information integrated in PBSMT as local and non-local features of MaxEntropy model. The experiment results with English-Vietnamese pair showed that our approach achieves significant improvements over the system which uses a lexical hierarchical reordering model .

Hierarchical phrase-based machine translation with word-based reordering model

Proceedings of the …, 2010

Hierarchical phrase-based machine translation 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 include reordered source strings, allowing efficient calculation of reordering ...

A simple and effective hierarchical phrase reordering model

Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08, 2008

While phrase-based statistical machine translation systems currently deliver state-of-theart performance, they remain weak on word order changes. Current phrase reordering models can properly handle swaps between adjacent phrases, but they typically lack the ability to perform the kind of long-distance reorderings possible with syntax-based systems. In this paper, we present a novel hierarchical phrase reordering model aimed at improving non-local reorderings, which seamlessly integrates with a standard phrase-based system with little loss of computational efficiency. We show that this model can successfully handle the key examples often used to motivate syntax-based systems, such as the rotation of a prepositional phrase around a noun phrase. We contrast our model with reordering models commonly used in phrase-based systems, and show that our approach provides statistically significant BLEU point gains for two language pairs: Chinese-English (+0.53 on MT05 and +0.71 on MT08) and Arabic-English (+0.55 on MT05).

Syntax- and semantic-based reordering in hierarchical phrase-based statistical machine translation

Expert Systems with Applications, 2017

We present a syntax-based reordering model (RM) for hierarchical phrase-based statistical machine translation (HPB-SMT) enriched with semantic features. Our model brings a number of novel contributions: (i) while the previous dependency-based RM is limited to the reordering of head and dependant constituent pairs, we also model the reordering of pairs of dependants; (ii) Our model is enriched with semantic features (Wordnet synsets) in order to allow the reordering model to generalize to pairs not seen in training but with equivalent meaning. (iii) We evaluate our model on two language directions: English-to-Farsi and English-to-Turkish. These language pairs are particularly challenging due to the free word order, rich morphology and lack of resources of the target languages. We evaluate our RM both intrinsically (accuracy of the RM classifier) and extrinsically (MT). Our best configuration outperforms the baseline classifier by 5-29% on pairs of dependants and by 12-30% on head and dependant pairs while the improvement on MT ranges between 1.6% and 5.5% relative in terms of BLEU depending on language pair and domain. We also analyze the value of the feature weights to obtain further insights on the impact of the reordering-related features in the HPB-SMT model. We observe that the features of our RM are assigned significant weights and that our features are complementary to the reordering feature included by default in the HPB-SMT model.

Generalizing Hierarchical Phrase-based Translation using Rules with Adjacent Nonterminals

2010

Hierarchical phrase-based translation (Hiero, (Chiang, 2005)) provides an attractive framework within which both short-and longdistance reorderings can be addressed consistently and ef ciently. However, Hiero is generally implemented with a constraint preventing the creation of rules with adjacent nonterminals, because such rules introduce computational and modeling challenges. We introduce methods to address these challenges, and demonstrate that rules with adjacent nonterminals can improve Hiero's generalization power and lead to signi cant performance gains in Chinese-English translation.

Handling phrase reorderings for machine translation

Proceedings of the ACL-IJCNLP 2009 Conference Short Papers on - ACL-IJCNLP '09, 2009

We propose a distance phrase reordering model (DPR) for statistical machine translation (SMT), where the aim is to capture phrase reorderings using a structure learning framework. On both the reordering classification and a Chinese-to-English translation task, we show improved performance over a baseline SMT system.

Head-driven hierarchical phrase-based translation

2012

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.

Hierarchical Phrase-Based Translation with Jane 2

The Prague Bulletin of Mathematical Linguistics, 2012

In this paper, we give a survey of several recent extensions to hierarchical phrase-based machine translation that have been implemented in version 2 of Jane, RWTH's open source statistical machine translation toolkit. We focus on the following techniques: Insertion and deletion models, lexical scoring variants, reordering extensions with non-lexicalized reordering rules and with a discriminative lexicalized reordering model, and soft string-to-dependency hierarchical machine translation. We describe the fundamentals of each of these techniques and present experimental results obtained with Jane 2 to confirm their usefulness in state-ofthe-art hierarchical phrase-based translation (HPBT).

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 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.

Accurate non-hierarchical phrase-based translation

2010

A principal weakness of conventional (i.e., non-hierarchical) phrase-based statistical machine translation is that it can only exploit continuous phrases. In this paper, we extend phrase-based decoding to allow both source and target phrasal discontinuities, which provide better generalization on unseen data and yield significant improvements to a standard phrase-based system (Moses). More interestingly, our discontinuous phrasebased system also outperforms a state-of-the-art hierarchical system (Joshua) by a very significant margin (+1.03 BLEU on average on five Chinese-English NIST test sets), even though both Joshua and our system support discontinuous phrases. Since the key difference between these two systems is that ours is not hierarchical-i.e., our system uses a string-based decoder instead of CKY, and it imposes no hard hierarchical reordering constraints during training and decoding-this paper sets out to challenge the commonly held belief that the tree-based parameterization of systems such as Hiero and Joshua is crucial to their good performance against Moses.