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

Proceedings

Proceedings of the NODALIDA 2011 Workshop Constraint Grammar Applications. Editors: Eckhard Bick, Kristin Hagen, Kaili Müürisep, Trond Trosterud. NEALT Proceedings Series, Vol. 14 (2011), vi+69 pp. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/19231

ANNOTATED DISJUNCT FOR MACHINE TRANSLATION

Most information found in the Internet is available in English version. However, most people in the world are non-English speaker. Hence, it will be of great advantage to have reliable Machine Translation tool for those people. There are many approaches for developing Machine Translation (MT) systems, some of them are direct, rule-based/transfer, interlingua, and statistical approaches. This thesis focuses on developing an MT for less resourced languages i.e. languages that do not have available grammar formalism, parser, and corpus, such as some languages in South East Asia. The nonexistence of bilingual corpora motivates us to use direct or transfer approaches. Moreover, the unavailability of grammar formalism and parser in the target languages motivates us to develop a hybrid between direct and transfer approaches. This hybrid approach is referred as a hybrid transfer approach. This approach uses the Annotated Disjunct (ADJ) method. This method, based on Link Grammar (LG) formalism, can theoretically handle one-to-one, many-to-one, and many-to-many word(s) translations. This method consists of transfer rules module which maps source words in a source sentence (SS) into target words in correct position in a target sentence (TS). The developed transfer rules are demonstrated on English → Indonesian translation tasks. An experimental evaluation is conducted to measure the performance of the developed system over available English-Indonesian MT systems. The developed ADJ-based MT system translated simple, compound, and complex English sentences in present, present continuous, present perfect, past, past perfect, and future tenses with better precision than other systems, with the accuracy of 71.17% in Subjective Sentence Error Rate metric

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

Translation of Pronominal Anaphora between English and Spanish: Discrepancies and Evaluation

This paper evaluates the different tasks carried out in the translation of pronominal anaphora in a machine translation (MT) system. The MT interlingua approach named AGIR (Anaphora Generation with an Interlingua Representation) improves upon other proposals presented to date because it is able to translate intersentential anaphors, detect co-reference chains, and translate Spanish zero pronouns into English---issues hardly considered by other systems. The paper presents the resolution and evaluation of these anaphora problems in AGIR with the use of different kinds of knowledge (lexical, morphological, syntactic, and semantic). The translation of English and Spanish anaphoric third-person personal pronouns (including Spanish zero pronouns) into the target language has been evaluated on unrestricted corpora. We have obtained a precision of 80.4% and 84.8% in the translation of Spanish and English pronouns, respectively. Although we have only studied the Spanish and English languages, our approach can be easily extended to other languages such as Portuguese, Italian, or Japanese