The TALP–UPC Spanish–English WMT biomedical task: bilingual embeddings and char-based neural language model rescoring in a phrase-based system

This paper describes the TALP–UPC system in the Spanish–English WMT 2016 biomedical shared task. Our system is a standard phrase-based system enhanced with vocabulary expansion using bilingual word embeddings and a characterbased neural language model with rescoring. The former focuses on resolving outof- vocabulary words, while the latter enhances the fluency of the system. The two modules progressively improve the final translation as measured by a combination of several lexical metrics.Postprint (published version

Lessons learned in multilingual grounded language learning

Recent work has shown how to learn better visual-semantic embeddings by leveraging image descriptions in more than one language. Here, we investigate in detail which conditions affect the performance of this type of grounded language learning model. We show that multilingual training improves over bilingual training, and that low-resource languages benefit from training with higher-resource languages. We demonstrate that a multilingual model can be trained equally well on either translations or comparable sentence pairs, and that annotating the same set of images in multiple language enables further improvements via an additional caption-caption ranking objective.Comment: CoNLL 201

LIUM Machine Translation Systems for WMT17 News Translation Task

This paper describes LIUM submissions to WMT17 News Translation Task for English-German, English-Turkish, English-Czech and English-Latvian language pairs. We train BPE-based attentive Neural Machine Translation systems with and without factored outputs using the open source nmtpy framework. Competitive scores were obtained by ensembling various systems and exploiting the availability of target monolingual corpora for back-translation. The impact of back-translation quantity and quality is also analyzed for English-Turkish where our post-deadline submission surpassed the best entry by +1.6 BLEU.Comment: News Translation Task System Description paper for WMT1