When machine translation researchers participate in evaluation tasks, they typically design their primary submissions using ideas that are not genre-specific. In fact, their systems look much the same from one evaluation campaign to another. In this paper, we analyze two popular genres: spoken language and written news, using publicly available corpora which stem from the popular WMT and IWSLT ...

This paper describes the integration of a Turkish generation system with the KANT knowledge-based machine translation system to produce a prototype English–Turkish interlingua-based machine translation system. These two independently constructed systems were successfully integrated within a period of two months, through development of a module which maps KANT interlingua expressions to Turkish ...

This paper describes the techniques we explored to improve the translation of news text in the German-English and Hungarian-English tracks of the WMT09 shared translation task. Beginning with a convention hierarchical phrase-based system, we found benefits for using word segmentation lattices as input, explicit generation of beginning and end of sentence markers, minimum Bayes risk decoding, an...

Multimodal machine translation is the task of translating sentences in a visual context. We decompose this problem into two sub-tasks: learning to translate and learning visually grounded representations. In a multitask learning framework, translations are learned in an attention-based encoderdecoder, and grounded representations are learned through image representation prediction. Our approach...

This paper describes the system we developed to improve German-English translation of News text for the shared task of the Fifth Workshop on Statistical Machine Translation. Working within cdec, an open source modular framework for machine translation, we explore the benefits of several modifications to our hierarchical phrase-based model, including segmentation lattices, minimum Bayes Risk dec...

We present a simple method to incorporate syntactic information about the target language in a neural machine translation system by translating into linearized, lexicalized constituency trees. Experiments on the WMT16 German-English news translation task shown improved BLEU scores when compared to a syntax-agnostic NMT baseline trained on the same dataset. An analysis of the translations from t...

This paper describes the techniques we explored to improve the translation of news text in the German-English and HungarianEnglish tracks of the WMT09 shared translation task. Beginning with a convention hierarchical phrase-based system, we found benefits for using word segmentation lattices as input, explicit generation of beginning and end of sentence markers, minimum Bayes risk decoding, and...

Many studies have been reported in the domain of speech-to-speech machine translation systems for travel conversation use. Therefore, a large number of travel domain corpora have become available in recent years. From a wider viewpoint, speech-to-speech systems are required for many purposes other than travel conversation. One of these is monologues (e.g., TV news, lectures, technical presentat...

This paper describes the statistical machine translation system developed at RWTH Aachen University for the English→Romanian translation task of the ACL 2016 First Conference on Machine Translation (WMT 2016). We combined three different state-ofthe-art systems in a system combination: A phrase-based system, a hierarchical phrase-based system and an attentionbased neural machine translation sys...