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        <title>Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder</title>
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        <namePart type="given">Fahim</namePart>
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        <namePart type="given">Nadir</namePart>
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        <namePart type="given">Hassan</namePart>
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    <name type="personal">
        <namePart type="given">Yonatan</namePart>
        <namePart type="family">Belinkov</namePart>
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    <name type="personal">
        <namePart type="given">Stephan</namePart>
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    <abstract>End-to-end training makes the neural machine translation (NMT) architecture simpler, yet elegant compared to traditional statistical machine translation (SMT). However, little is known about linguistic patterns of morphology, syntax and semantics learned during the training of NMT systems, and more importantly, which parts of the architecture are responsible for learning each of these phenomenon. In this paper we i) analyze how much morphology an NMT decoder learns, and ii) investigate whether injecting target morphology in the decoder helps it to produce better translations. To this end we present three methods: i) simultaneous translation, ii) joint-data learning, and iii) multi-task learning. Our results show that explicit morphological information helps the decoder learn target language morphology and improves the translation quality by 0.2–0.6 BLEU points.</abstract>
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%0 Conference Proceedings
%T Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder
%A Dalvi, Fahim
%A Durrani, Nadir
%A Sajjad, Hassan
%A Belinkov, Yonatan
%A Vogel, Stephan
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F dalvi-etal-2017-understanding
%X End-to-end training makes the neural machine translation (NMT) architecture simpler, yet elegant compared to traditional statistical machine translation (SMT). However, little is known about linguistic patterns of morphology, syntax and semantics learned during the training of NMT systems, and more importantly, which parts of the architecture are responsible for learning each of these phenomenon. In this paper we i) analyze how much morphology an NMT decoder learns, and ii) investigate whether injecting target morphology in the decoder helps it to produce better translations. To this end we present three methods: i) simultaneous translation, ii) joint-data learning, and iii) multi-task learning. Our results show that explicit morphological information helps the decoder learn target language morphology and improves the translation quality by 0.2–0.6 BLEU points.
%U https://aclanthology.org/I17-1015
%P 142-151

Markdown (Informal)

[Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder](https://aclanthology.org/I17-1015) (Dalvi et al., IJCNLP 2017)

• Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder (Dalvi et al., IJCNLP 2017)

ACL

• Fahim Dalvi, Nadir Durrani, Hassan Sajjad, Yonatan Belinkov, and Stephan Vogel. 2017. Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 142–151, Taipei, Taiwan. Asian Federation of Natural Language Processing.