The ability to quickly incorporate incoming training data into a running translation system is critical in a number of applications. Mechanisms based on incremental model update and the online EM algorithm hold the promise of achieving this objective in a principled way. Still, efficient tools for incremental training are yet to be available. In this paper we experiment with simple alternative solutions for interim model updates, within the popular Moses system. Short of updating the model in real time, such updates can execute in short timeframes even when operating on large models, and achieve a performance level close to, and in some cases exceeding, that of batch retraining.

In this paper we explore a task-driven approach to interfacing NLP components, where language processing is guided by the end-task that each application requires. The core idea is to generalize feature values into feature value distributions, representing under-specified feature values, and to fit linguistic pipelines with a back-channel of specification requests through which subsequent components can declare to preceding ones the importance of narrowing the value distribution of particular features that are critical for the current task.

We describe a dataset containing 16,000 translations produced by four machine translation systems and manually annotated for quality by professional translators. This dataset can be used in a range of tasks assessing machine translation evaluation metrics, from basic correlation analysis to training and test of machine learning-based metrics. By providing a standard dataset for such tasks, we hope to encourage the development of better MT evaluation metrics.

We present a conditional-random-field approach to discriminatively-trained phrase-based machine translation in which training and decoding are both cast in a sampling framework and are implemented uniformly in a new probabilistic programming language for factor graphs. In traditional phrase-based translation, decoding infers both a "Viterbi" alignment and the target sentence. In contrast, in our approach, a rich overlapping-phrase alignment is produced by a fast deterministic method, while probabilistic decoding infers only the target sentence, which is then able to leverage arbitrary features of the entire source sentence, target sentence and alignment. By using SampleRank for learning we could in principle efficiently estimate hundreds of thousands of parameters. Test-time decoding is done by MCMC sampling with annealing. To demonstrate the potential of our approach we show preliminary experiments leveraging alignments that may contain overlapping bi-phrases.

Cet article présente une méthode de traduction automatique statistique basée sur des segments non-continus, c’est-à-dire des segments formés de mots qui ne se présentent pas nécéssairement de façon contiguë dans le texte. On propose une méthode pour produire de tels segments à partir de corpus alignés au niveau des mots. On présente également un modèle de traduction statistique capable de tenir compte de tels segments, de même qu’une méthode d’apprentissage des paramètres du modèle visant à maximiser l’exactitude des traductions produites, telle que mesurée avec la métrique NIST. Les traductions optimales sont produites par le biais d’une recherche en faisceau. On présente finalement des résultats expérimentaux, qui démontrent comment la méthode proposée permet une meilleure généralisation à partir des données d’entraînement.