A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena

Word reordering is one of the most difficult aspects of statistical machine translation (SMT), and an important factor of its quality and efficiency. Despite the vast amount of research published to date, the interest of the community in this problem has not decreased, and no single method appears to be strongly dominant across language pairs. Instead, the choice of the optimal approach for a new translation task still seems to be mostly driven by empirical trials. To orientate the reader in this vast and complex research area, we present a comprehensive survey of word reordering viewed as a statistical modeling challenge and as a natural language phenomenon. The survey describes in detail how word reordering is modeled within different string-based and tree-based SMT frameworks and as a stand-alone task, including systematic overviews of the literature in advanced reordering modeling. We then question why some approaches are more successful than others in different language pairs. We argue that, besides measuring the amount of reordering, it is important to understand which kinds of reordering occur in a given language pair. To this end, we conduct a qualitative analysis of word reordering phenomena in a diverse sample of language pairs, based on a large collection of linguistic knowledge. Empirical results in the SMT literature are shown to support the hypothesis that a few linguistic facts can be very useful to anticipate the reordering characteristics of a language pair and to select the SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic

Getting Past the Language Gap: Innovations in Machine Translation

In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT

Improved Chinese Language Processing for an Open Source Search Engine

Natural Language Processing (NLP) is the process of computers analyzing on human languages. There are also many areas in NLP. Some of the areas include speech recognition, natural language understanding, and natural language generation. Information retrieval and natural language processing for Asians languages has its own unique set of challenges not present for Indo-European languages. Some of these are text segmentation, named entity recognition in unsegmented text, and part of speech tagging. In this report, we describe our implementation of and experiments with improving the Chinese language processing sub-component of an open source search engine, Yioop. In particular, we rewrote and improved the following sub-systems of Yioop to try to make them as state-of-the-art as possible: Chinese text segmentation, Part-of-speech (POS) tagging, Named Entity Recognition (NER), and Question and Answering System. Compared to the previous system we had a 9% improvement on Chinese words Segmentation accuracy. We built POS tagging with 89% accuracy. And We implement NER System with 76% accuracy

A novel dependency-based evaluation metric for machine translation

Automatic evaluation measures such as BLEU (Papineni et al. (2002)) and NIST (Doddington (2002)) are indispensable in the development of Machine Translation (MT) systems, because they allow MT developers to conduct frequent, fast, and cost-effective evaluations of their evolving translation models. However, most of the automatic evaluation metrics rely on a comparison of word strings, measuring only the surface similarity of the candidate and reference translations, and will penalize any divergence. In effect,a candidate translation expressing the source meaning accurately and fluently will be given a low score if the lexical and syntactic choices it contains, even though perfectly legitimate, are not present in at least one of the references. Necessarily, this score would differ from a much more favourable human judgment that such a translation would receive. This thesis presents a method that automatically evaluates the quality of translation based on the labelled dependency structure of the sentence, rather than on its surface form. Dependencies abstract away from the some of the particulars of the surface string realization and provide a more "normalized" representation of (some) syntactic variants of a given sentence. The translation and reference files are analyzed by a treebank-based, probabilistic Lexical-Functional Grammar (LFG) parser (Cahill et al. (2004)) for English, which produces a set of dependency triples for each input. The translation set is compared to the reference set, and the number of matches is calculated, giving the precision, recall, and f-score for that particular translation. The use of WordNet synonyms and partial matching during the evaluation process allows for adequate treatment of lexical variation, while employing a number of best parses helps neutralize the noise introduced during the parsing stage. The dependency-based method is compared against a number of other popular MT evaluation metrics, including BLEU, NIST, GTM (Turian et al. (2003)), TER (Snover et al. (2006)), and METEOR (Banerjee and Lavie (2005)), in terms of segment- and system-level correlations with human judgments of fluency and adequacy. We also examine whether it shows bias towards statistical MT models. The comparison of the dependency-based method with other evaluation metrics is then extended to languages other than English: French, German, Spanish, and Japanese, where we apply our method to dependencies generated by Microsoft's NLPWin analyzer (Corston-Oliver and Dolan (1999); Heidorn (2000)) as well as, in the case of the Spanish data, those produced by the treebank-based, probabilistic LFG parser of Chrupa la and van Genabith (2006a,b)

Integrating source-language context into log-linear models of statistical machine translation

The translation features typically used in state-of-the-art statistical machine translation (SMT) model dependencies between the source and target phrases, but not among the phrases in the source language themselves. A swathe of research has demonstrated that integrating source context modelling directly into log-linear phrase-based SMT (PB-SMT) and hierarchical PB-SMT (HPB-SMT), and can positively influence the weighting and selection of target phrases, and thus improve translation quality. In this thesis we present novel approaches to incorporate source-language contextual modelling into the state-of-the-art SMT models in order to enhance the quality of lexical selection. We investigate the effectiveness of use of a range of contextual features, including lexical features of neighbouring words, part-of-speech tags, supertags, sentence-similarity features, dependency information, and semantic roles. We explored a series of language pairs featuring typologically different languages, and examined the scalability of our research to larger amounts of training data. While our results are mixed across feature selections, language pairs, and learning curves, we observe that including contextual features of the source sentence in general produces improvements. The most significant improvements involve the integration of long-distance contextual features, such as dependency relations in combination with part-of-speech tags in Dutch-to-English subtitle translation, the combination of dependency parse and semantic role information in English-to-Dutch parliamentary debate translation, supertag features in English-to-Chinese translation, or combination of supertag and lexical features in English-to-Dutch subtitle translation. Furthermore, we investigate the applicability of our lexical contextual model in another closely related NLP problem, namely machine transliteration

Universal Syntactic Structures: Modeling Syntax for Various Natural Languages

We aim to provide an explanation for how the human brain might connect words for sentence formation. A novel approach to modeling syntactic representation is introduced, potentially showing the existence of universal syntactic structures for all natural languages. As the discovery of DNA's double helix structure shed light on the inner workings of genetics, we wish to introduce a basic understanding of how language might work in the human brain. It could be the brain's way of encoding and decoding knowledge. It also brings some insight into theories in linguistics, psychology, and cognitive science. After looking into the logic behind universal syntactic structures and the methodology of the modeling technique, we attempt to analyze corpora that showcase universality in the language process of different natural languages such as English and Korean. Lastly, we discuss the critical period hypothesis, universal grammar, and a few other assertions on language for the purpose of advancing our understanding of the human brain.Comment: 22 pages, 9 figure

Abstract syntax as interlingua: Scaling up the grammatical framework from controlled languages to robust pipelines

Syntax is an interlingual representation used in compilers. Grammatical Framework (GF) applies the abstract syntax idea to natural languages. The development of GF started in 1998, first as a tool for controlled language implementations, where it has gained an established position in both academic and commercial projects. GF provides grammar resources for over 40 languages, enabling accurate generation and translation, as well as grammar engineering tools and components for mobile and Web applications. On the research side, the focus in the last ten years has been on scaling up GF to wide-coverage language processing. The concept of abstract syntax offers a unified view on many other approaches: Universal Dependencies, WordNets, FrameNets, Construction Grammars, and Abstract Meaning Representations. This makes it possible for GF to utilize data from the other approaches and to build robust pipelines. In return, GF can contribute to data-driven approaches by methods to transfer resources from one language to others, to augment data by rule-based generation, to check the consistency of hand-annotated corpora, and to pipe analyses into high-precision semantic back ends. This article gives an overview of the use of abstract syntax as interlingua through both established and emerging NLP applications involving GF