This paper presents the prototype and the preliminary evaluation of an automatic translation system developed in the LIS4ALL project. The system domain is the corpus of railway station announcements in Italian. The output of the system is a 3D animated avatar that signs announcements in Italian Sign Language. The preliminary evaluation, which measures the accuracy of the translations at the sen...

We present a novel online learning approach for statistical machine translation tailored to the computer assisted translation scenario. With the introduction of a simple online feature, we are able to adapt the translation model on the fly to the corrections made by the translators. Additionally, we do online adaption of the feature weights with a large margin algorithm. Our results show that o...

Minimum error rate training (MERT) is a widely used learning procedure for statistical machine translation models. We contrast three search strategies for MERT: Powell’s method, the variant of coordinate descent found in the Moses MERT utility, and a novel stochastic method. It is shown that the stochastic method obtains test set gains of +0.98 BLEU on MT03 and +0.61 BLEU on MT05. We also prese...

This paper describes the UPM system for the Spanish-English translation task at the NAACL 2012 workshop on statistical machine translation. This system is based on Moses. We have used all available free corpora, cleaning and deleting some repetitions. In this paper, we also propose a technique for selecting the sentences for tuning the system. This technique is based on the similarity with the ...

Automatic tools for machine translation (MT) evaluation such as BLEU are well established, but have the drawbacks that they do not perform well at the sentence level and that they presuppose manually translated reference texts. Assuming that the MT system to be evaluated can deal with both directions of a language pair, in this research we suggest to conduct automatic MT evaluation by determini...

This paper proposes to use monolingual collocations to improve Statistical Machine Translation (SMT). We make use of the collocation probabilities, which are estimated from monolingual corpora, in two aspects, namely improving word alignment for various kinds of SMT systems and improving phrase table for phrase-based SMT. The experimental results show that our method improves the performance of...

The parameters of statistical translation models are typically estimated from sentence-aligned parallel corpora. We show that significant improvements in the alignment and translation quality of such models can be achieved by additionally including wordaligned data during training. Incorporating wordlevel alignments into the parameter estimation of the IBM models reduces alignment error rate an...

System combination exploits differences between machine translation systems to form a combined translation from several system outputs. Core to this process are features that reward n-gram matches between a candidate combination and each system output. Systems differ in performance at the n-gram level despite similar overall scores. We therefore advocate a new feature formulation: for each syst...

We improve translation memory (TM)inspired consistent phrase-based statistical machine translation (PB-SMT) using rich linguistic information including lexical, part-of-speech, dependency, and semantic role features to predict whether a TM-derived sub-segment should constrain PB-SMT translation. Besides better translation consistency, for English-to-Chinese Symantec TMs we report a 1.01 BLEU po...

This paper describes our submissions to the machine translation evaluation shared task in ACL WMT-08. Our primary submission is the Meteor metric tuned for optimizing correlation with human rankings of translation hypotheses. We show significant improvement in correlation as compared to the earlier version of metric which was tuned to optimized correlation with traditional adequacy and fluency ...