Faster Phrase-Based Decoding by Refining Feature State

We contribute a faster decoding algo-rithm for phrase-based machine transla-tion. Translation hypotheses keep track of state, such as context for the language model and coverage of words in the source sentence. Most features depend upon only part of the state, but traditional algorithms, including cube pruning, handle state atom-ically. For example, cube pruning will re-peatedly query the language model with hypotheses that differ only in source cov-erage, despite the fact that source cover-age is irrelevant to the language model. Our key contribution avoids this behav-ior by placing hypotheses into equivalence classes, masking the parts of state that matter least to the score. Moreover, we ex-ploit shared words in hypotheses to itera-tively refine language model scores rather than handling language model state atom-ically. Since our algorithm and cube prun-ing are both approximate, improvement can be used to increase speed or accuracy. When tuned to attain the same accuracy, our algorithm is 4.0–7.7 times as fast as the Moses decoder with cube pruning.

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

dynamically shaping the reordering search space of phrase based statistical machine translation

Defining the reordering search space is a crucial issue in phrase-based SMT between distant languages. In fact, the optimal trade-off between accuracy and complexity of decoding is nowadays reached by harshly limiting the input permutation space. We propose a method to dynamically shape such space and, thus, capture long-range word movements without hurting translation quality nor decoding time. The space defined by loose reordering constraints is dynamically pruned through a binary classifier that predicts whether a given input word should be translated right after another. The integration of this model into a phrase-based decoder improves a strong Arabic-English baseline already including state-of-the-art early distortion cost (Moore and Quirk, 2007) and hierarchical phrase orientation models (Galley and Manning, 2008). Significant improvements in the reordering of verbs are achieved by a system that is notably faster than the baseline, while bleu and meteor remain stable, or even increase, at a very high distortion limit

Exact decoding for phrase-based statistical machine translation

© 2014 Association for Computational Linguistics. The combinatorial space of translation derivations in phrase-based statistical machine translation is given by the intersection between a translation lattice and a target language model. We replace this intractable intersection by a tractable relaxation which incorporates a low-order upperbound on the language model. Exact optimisation is achieved through a coarseto- fine strategy with connections to adaptive rejection sampling. We perform exact optimisation with unpruned language models of order 3 to 5 and show searcherror curves for beam search and cube pruning on standard test sets. This is the first work to tractably tackle exact optimisation with language models of orders higher than 3