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        <title>Guided Alignment Training for Topic-Aware Neural Machine Translation</title>
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    <abstract>In this paper, we propose an effective way for biasing the attention mechanism of a sequence-to-sequence neural machine translation (NMT) model towards the well-studied statistical word alignment models. We show that our novel guided alignment training approach improves translation quality on real-life e-commerce texts consisting of product titles and descriptions, overcoming the problems posed by many unknown words and a large type/token ratio. We also show that meta-data associated with input texts such as topic or category information can significantly improve translation quality when used as an additional signal to the decoder part of the network. With both novel features, the BLEU score of the NMT system on a product title set improves from 18.6 to 21.3%. Even larger MT quality gains are obtained through domain adaptation of a general domain NMT system to e-commerce data. The developed NMT system also performs well on the IWSLT speech translation task, where an ensemble of four variant systems outperforms the phrase-based baseline by 2.1% BLEU absolute.</abstract>
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%0 Conference Proceedings
%T Guided Alignment Training for Topic-Aware Neural Machine Translation
%A Chen, Wenhu
%A Matusov, Evgeny
%A Khadivi, Shahram
%A Peter, Jan-Thorsten
%Y Green, Spence
%Y Schwartz, Lane
%S Conferences of the Association for Machine Translation in the Americas: MT Researchers’ Track
%D 2016
%8 oct 28  nov 1
%I The Association for Machine Translation in the Americas
%C Austin, TX, USA
%F chen-etal-2016-guided
%X In this paper, we propose an effective way for biasing the attention mechanism of a sequence-to-sequence neural machine translation (NMT) model towards the well-studied statistical word alignment models. We show that our novel guided alignment training approach improves translation quality on real-life e-commerce texts consisting of product titles and descriptions, overcoming the problems posed by many unknown words and a large type/token ratio. We also show that meta-data associated with input texts such as topic or category information can significantly improve translation quality when used as an additional signal to the decoder part of the network. With both novel features, the BLEU score of the NMT system on a product title set improves from 18.6 to 21.3%. Even larger MT quality gains are obtained through domain adaptation of a general domain NMT system to e-commerce data. The developed NMT system also performs well on the IWSLT speech translation task, where an ensemble of four variant systems outperforms the phrase-based baseline by 2.1% BLEU absolute.
%U https://aclanthology.org/2016.amta-researchers.10
%P 121-134

Markdown (Informal)

[Guided Alignment Training for Topic-Aware Neural Machine Translation](https://aclanthology.org/2016.amta-researchers.10) (Chen et al., AMTA 2016)

• Guided Alignment Training for Topic-Aware Neural Machine Translation (Chen et al., AMTA 2016)

ACL

• Wenhu Chen, Evgeny Matusov, Shahram Khadivi, and Jan-Thorsten Peter. 2016. Guided Alignment Training for Topic-Aware Neural Machine Translation. In Conferences of the Association for Machine Translation in the Americas: MT Researchers' Track, pages 121–134, Austin, TX, USA. The Association for Machine Translation in the Americas.