We tried to cope with the complex morphology of Turkish by applying different schemes of morphological word segmentation to the training and test data of a phrase-based statistical machine translation system. These techniques allow for a considerable reduction of the training dictionary, and lower the out-of-vocabulary rate of the test set. By minimizing differences between lexical granularities of Turkish and English we can produce more refined alignments and a better modeling of the translation task. Morphological segmentation is highly language dependent and requires a fair amount of linguistic knowledge in its development phase. Yet it is fast and light-weight – does not involve syntax – and appears to benefit our IWSLT09 system: our best segmentation scheme associated to a simple lexical approximation technique achieved a 50% reduction of out-of-vocabulary rate and over 5 point BLEU improvement above the baseline.

In this paper, we propose a new method for training translation rules for a Synchronous Context-free Grammar. A bilingual chart parser is used to generate the parse forest, and EM algorithm to estimate expected counts for each rule of the ruleset. Additional rules are constructed as combinations of reliable rules occurring in the parse forest. The new method of proposing additional translation rules is independent of word alignments. We present the theoretical background for this method, and initial experimental results on German-English translations of Europarl data.

Minimum error rate training (MERT) is a widely used learning method for statistical machine translation. In this paper, we present a SVM-based training method to enhance generalization ability. We extend MERT optimization by maximizing the margin between the reference and incorrect translations under the L2-norm prior to avoid overfitting problem. Translation accuracy obtained by our proposed methods is more stable in various conditions than that obtained by MERT. Our experimental results on the French-English WMT08 shared task show that degrade of our proposed methods is smaller than that of MERT in case of small training data or out-of-domain test data.

Despite many differences between phrase-based, hierarchical, and syntax-based translation models, their training and testing pipelines are strikingly similar. Drawing on this fact, we extend the Moses toolkit to implement hierarchical and syntactic models, making it the first open source toolkit with end-to-end support for all three of these popular models in a single package. This extension substantially lowers the barrier to entry for machine translation research across multiple models.

This paper focuses on the problem of language model adaptation in the context of Chinese-English cross-lingual dialogs, as set-up by the challenge task of the IWSLT 2009 Evaluation Campaign. Mixtures of n-gram language models are investigated, which are obtained by clustering bilingual training data according to different available human annotations, respectively, at the dialog level, turn level, and dialog act level. For the latter case, clustering of IWSLT data was in fact induced through a comparable Italian-English parallel corpus provided with dialog act annotations. For the sake of adaptation, mixture weight estimation is performed either at the level of single source sentence or test set. Estimated weights are then transferred to the target language mixture model. Experimental results show that, by training different specific language models weighted according to the actual input instead of using a single target language model, significant gains in terms of perplexity and BLEU can be achieved.

This demo shows the network-based speech-to-speech translation system. The system was designed to perform realtime, location-free, multi-party translation between speakers of different languages. The spoken language modules: automatic speech recognition (ASR), machine translation (MT), and text-to-speech synthesis (TTS), are connected through Web servers that can be accessed via client applications worldwide. In this demo, we will show the multiparty speech-to-speech translation of Japanese, Chinese, Indonesian, Vietnamese, and English, provided by the NICT server. These speech-to-speech modules have been developed by NICT as a part of A-STAR (Asian Speech Translation Advanced Research) consortium project1.