Articles

In this paper, we describe the TALP- UPC participation in the News Task for German-English and Finish-English. Our primary submission implements a fully character to character neural machine translation architecture with an additional rescoring of a n-best list of hypothesis us- ing a forced back-translation to the source sentence. This …...

This paper describes the Johns Hop- kins University submissions to the shared translation task of EMNLP 2017 Sec- ond Conference on Machine Translation (WMT 2017). We set up phrase-based, syntax-based and/or neural machine trans- lation systems for all 14 language pairs of this year’s evaluation campaign. We also performed neural …...

This paper describes the AFRL-MITLL machine translation systems and the im- provements that were developed during the WMT17 evaluation campaign. This year, we explore the continuing prolifera- tion of Neural Machine Translation toolk- its, revisit our previous data-selection ef- forts for use in training systems with these new toolkits and …...

This paper describes LIMSI’s submissions to the news shared task at WMT’17 for English into Czech and Latvian, as well as related experiments. This year’s novel- ties consist in the use of a neural machine translation system with a factored output predicting simultaneously a lemma deco- rated with morphological information …...

This paper describes the neural and phrase-based machine translation systems submitted by CUNI to English-Czech News Translation Task of WMT17. We experiment with synthetic data for training and try several system combination tech- niques, both neural and phrase-based. Our primary submission CU-CHIMERA ends up being phrase-based backbone which in- corporates …...

Automatic translation of documents is an important task in many domains, in- cluding the biological and clinical do- mains. The second edition of the Biomed- ical Translation task in the Conference of Machine Translation focused on the au- tomatic translation of biomedical-related documents between English and various European languages. This …...

We present the results from the second shared task on multimodal machine trans- lation and multilingual image description. Nine teams submitted 19 systems to two tasks. The multimodal translation task, in which the source sentence is supplemented by an image, was extended with a new lan- guage (French) and two …...

This paper presents the results of the WMT17 shared tasks, which included three machine translation (MT) tasks (news, biomedical, and multimodal), two evaluation tasks (metrics and run-time es- timation of MT quality), an automatic post-editing task, a neural MT training task, and a bandit learning task. 1...

In this paper we report on FBK’s par- ticipation to the English-to-German news translation task of the Second Conference on Machine Translation (WMT’17). The submitted system is based on Neural Ma- chine Translation using byte-pair encod- ing segmentation on both source and tar- get languages for open-vocabulary trans- lations. Back-translations …...

Differently from the phrase-based paradigm, neural machine translation (NMT) operates on word and sentence representations in a continuous space. This makes the decoding process not only more difficult to interpret, but also harder to influence with external knowledge. For the latter problem, effective solutions like the XML-markup used by phrase-based …...

We train a neural machine translation (NMT) system to both translate source- language text and copy target-language text, thereby exploiting monolingual cor- pora in the target language. Specifically, we create a bitext from the monolingual text in the target language so that each source sentence is identical to the tar- …...

This paper describes SYSTRAN’s sys- tems submitted to the WMT 2017 shared news translation task for English-German, in both translation directions. Our sys- tems are built using OpenNMT1, an open- source neural machine translation system, implementing sequence-to-sequence mod- els with LSTM encoder/decoders and at- tention. We experimented using mono- lingual …...

Recent works have shown that the usage of a synthetic parallel corpus can be effec- tively exploited by a neural machine trans- lation system. In this paper, we propose a new method for adapting a general neu- ral machine translation system to a specific task, by exploiting synthetic data. The …...

We investigate the application of Neu- ral Machine Translation (NMT) under the following three conditions posed by real- world application scenarios. First, we op- erate with an input stream of sentences coming from many different domains and with no predefined order. Second, the sen- tences are presented without domain in- …...

Neural Machine Translation (NMT) models are often trained on hetero- geneous mixtures of domains, from news to parliamentary proceedings, each with unique distributions and lan- guage. In this work we show that train- ing NMT systems on naively mixed data can degrade performance versus models fit to each constituent domain. …...

This work explores extending attention- based neural models to include alignment information as input. We modify the at- tention component to have dependence on the current source position. The at- tention model is then used as a lexical model together with an additional align- ment model to generate translation. The …...

It has been shown that increasing model depth improves the quality of neural ma- chine translation. However, different architectural variants to increase model depth have been proposed, and so far, there has been no thorough comparative study. In this work, we describe and evaluate several existing approaches to introduce depth …...

Pivot translation is a useful method for translating between languages with little or no parallel data by utilizing parallel data in an intermediate language such as En- glish. A popular approach for pivot trans- lation used in phrase-based or tree-based translation models combines source-pivot and pivot-target translation models into a …...

Linguistic resources such as part-of- speech (POS) tags have been extensively used in statistical machine translation (SMT) frameworks and have yielded bet- ter performances. However, usage of such linguistic annotations in neural machine translation (NMT) systems has been left under-explored. In this work, we show that multi-task learning is a …...

Neural machine translation (NMT) mod- els are able to partially learn syntactic in- formation from sequential lexical informa- tion. Still, some complex syntactic phe- nomena such as prepositional phrase at- tachment are poorly modeled. This work aims to answer two questions: 1) Does explicitly modeling target language syntax help NMT? …...