Better word alignments with supervised ITG models

This work investigates supervised word align-ment methods that exploit inversion transduc-tion grammar (ITG) constraints. We con-sider maximum margin and conditional like-lihood objectives, including the presentation of a new normal form grammar for canoni-calizing derivations. Even for non-ITG sen-tence pairs, we show that it is possible learn ITG alignment models by simple relaxations of structured discriminative learning objec-tives. For efficiency, we describe a set of prun-ing techniques that together allow us to align sentences two orders of magnitude faster than naive bitext CKY parsing. Finally, we intro-duce many-to-one block alignment features, which significantly improve our ITG models. Altogether, our method results in the best re-ported AER numbers for Chinese-English and a performance improvement of 1.1 BLEU over GIZA++ alignments.

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

Online Incremental Machine Translation

In this thesis we investigate the automatic improvements of statistical machine translation systems at runtime based on user feedback. We also propose a framework to use the proposed algorithms in large scale translation settings

A Comparative Study on Reordering Constraints in Statistical Machine Translation

In statistical machine translation, the generation of a translation hypothesis is computationally expensive. If arbitrary wordreorderings are permitted, the search problem is NP-hard. On the other hand, if we restrict the possible word-reorderings in an appropriate way, we obtain a polynomial-time search algorithm