Reordering in statistical machine translation

Abstract

Machine translation is a challenging task that its difficulties arise from several characteristics of natural language. The main focus of this work is on reordering as one of the major problems in MT and statistical MT, which is the method investigated in this research. The reordering problem in SMT originates from the fact that not all the words in a sentence can be consecutively translated. This means words must be skipped and be translated out of their order in the source sentence to produce a fluent and grammatically correct sentence in the target language. The main reason that reordering is needed is the fundamental word order differences between languages. Therefore, reordering becomes a more dominant issue, the more source and target languages are structurally different. The aim of this thesis is to study the reordering phenomenon by proposing new methods of dealing with reordering in SMT decoders and evaluating the effectiveness of the methods and the importance of reordering in the context of natural language processing tasks. In other words, we propose novel ways of performing the decoding to improve the reordering capabilities of the SMT decoder and in addition we explore the effect of improving the reordering on the quality of specific NLP tasks, namely named entity recognition and cross-lingual text association. Meanwhile, we go beyond reordering in text association and present a method to perform cross-lingual text fragment alignment, based on models of divergence from randomness. The main contribution of this thesis is a novel method named dynamic distortion, which is designed to improve the ability of the phrase-based decoder in performing reordering by adjusting the distortion parameter based on the translation context. The model employs a discriminative reordering model, which is combining several fea- 2 tures including lexical and syntactic, to predict the necessary distortion limit for each sentence and each hypothesis expansion. The discriminative reordering model is also integrated into the decoder as an extra feature. The method achieves substantial improvements over the baseline without increase in the decoding time by avoiding reordering in unnecessary positions. Another novel method is also presented to extend the phrase-based decoder to dynamically chunk, reorder, and apply phrase translations in tandem. Words inside the chunks are moved together to enable the decoder to make long-distance reorderings to capture the word order differences between languages with different sentence structures. Another aspect of this work is the task-based evaluation of the reordering methods and other translation algorithms used in the phrase-based SMT systems. With more successful SMT systems, performing multi-lingual and cross-lingual tasks through translating becomes more feasible. We have devised a method to evaluate the performance of state-of-the art named entity recognisers on the text translated by a SMT decoder. Specifically, we investigated the effect of word reordering and incorporating reordering models in improving the quality of named entity extraction. In addition to empirically investigating the effect of translation in the context of crosslingual document association, we have described a text fragment alignment algorithm to find sections of the two documents in different languages, that are content-wise related. The algorithm uses similarity measures based on divergence from randomness and word-based translation models to perform text fragment alignment on a collection of documents in two different languages. All the methods proposed in this thesis are extensively empirically examined. We have tested all the algorithms on common translation collections used in different evaluation campaigns. Well known automatic evaluation metrics are used to compare the suggested methods to a state-of-the art baseline and results are analysed and discussed.