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        <title>Understanding Neural Machine Translation by Simplification: The Case of Encoder-free Models</title>
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            <namePart type="family">Mitkov</namePart>
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            <namePart type="family">Angelova</namePart>
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    <abstract>In this paper, we try to understand neural machine translation (NMT) via simplifying NMT architectures and training encoder-free NMT models. In an encoder-free model, the sums of word embeddings and positional embeddings represent the source. The decoder is a standard Transformer or recurrent neural network that directly attends to embeddings via attention mechanisms. Experimental results show (1) that the attention mechanism in encoder-free models acts as a strong feature extractor, (2) that the word embeddings in encoder-free models are competitive to those in conventional models, (3) that non-contextualized source representations lead to a big performance drop, and (4) that encoder-free models have different effects on alignment quality for German-English and Chinese-English.</abstract>
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    <identifier type="doi">10.26615/978-954-452-056-4_136</identifier>
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%0 Conference Proceedings
%T Understanding Neural Machine Translation by Simplification: The Case of Encoder-free Models
%A Tang, Gongbo
%A Sennrich, Rico
%A Nivre, Joakim
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F tang-etal-2019-understanding
%X In this paper, we try to understand neural machine translation (NMT) via simplifying NMT architectures and training encoder-free NMT models. In an encoder-free model, the sums of word embeddings and positional embeddings represent the source. The decoder is a standard Transformer or recurrent neural network that directly attends to embeddings via attention mechanisms. Experimental results show (1) that the attention mechanism in encoder-free models acts as a strong feature extractor, (2) that the word embeddings in encoder-free models are competitive to those in conventional models, (3) that non-contextualized source representations lead to a big performance drop, and (4) that encoder-free models have different effects on alignment quality for German-English and Chinese-English.
%R 10.26615/978-954-452-056-4_136
%U https://aclanthology.org/R19-1136/
%U https://doi.org/10.26615/978-954-452-056-4_136
%P 1186-1193

Markdown (Informal)

[Understanding Neural Machine Translation by Simplification: The Case of Encoder-free Models](https://aclanthology.org/R19-1136/) (Tang et al., RANLP 2019)

• Understanding Neural Machine Translation by Simplification: The Case of Encoder-free Models (Tang et al., RANLP 2019)

ACL

• Gongbo Tang, Rico Sennrich, and Joakim Nivre. 2019. Understanding Neural Machine Translation by Simplification: The Case of Encoder-free Models. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 1186–1193, Varna, Bulgaria. INCOMA Ltd..