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        <title>Multilingual Neural Machine Translation with Task-Specific Attention</title>
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        <namePart type="given">Graeme</namePart>
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    <abstract>Multilingual machine translation addresses the task of translating between multiple source and target languages. We propose task-specific attention models, a simple but effective technique for improving the quality of sequence-to-sequence neural multilingual translation. Our approach seeks to retain as much of the parameter sharing generalization of NMT models as possible, while still allowing for language-specific specialization of the attention model to a particular language-pair or task. Our experiments on four languages of the Europarl corpus show that using a target-specific model of attention provides consistent gains in translation quality for all possible translation directions, compared to a model in which all parameters are shared. We observe improved translation quality even in the (extreme) low-resource zero-shot translation directions for which the model never saw explicitly paired parallel data.</abstract>
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%0 Conference Proceedings
%T Multilingual Neural Machine Translation with Task-Specific Attention
%A Blackwood, Graeme
%A Ballesteros, Miguel
%A Ward, Todd
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F blackwood-etal-2018-multilingual
%X Multilingual machine translation addresses the task of translating between multiple source and target languages. We propose task-specific attention models, a simple but effective technique for improving the quality of sequence-to-sequence neural multilingual translation. Our approach seeks to retain as much of the parameter sharing generalization of NMT models as possible, while still allowing for language-specific specialization of the attention model to a particular language-pair or task. Our experiments on four languages of the Europarl corpus show that using a target-specific model of attention provides consistent gains in translation quality for all possible translation directions, compared to a model in which all parameters are shared. We observe improved translation quality even in the (extreme) low-resource zero-shot translation directions for which the model never saw explicitly paired parallel data.
%U https://aclanthology.org/C18-1263
%P 3112-3122

Markdown (Informal)

[Multilingual Neural Machine Translation with Task-Specific Attention](https://aclanthology.org/C18-1263) (Blackwood et al., COLING 2018)

• Multilingual Neural Machine Translation with Task-Specific Attention (Blackwood et al., COLING 2018)

ACL

• Graeme Blackwood, Miguel Ballesteros, and Todd Ward. 2018. Multilingual Neural Machine Translation with Task-Specific Attention. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3112–3122, Santa Fe, New Mexico, USA. Association for Computational Linguistics.