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    <abstract>Encoder-decoder models typically only employ words that are frequently used in the training corpus because of the computational costs and/or to exclude noisy words. However, this vocabulary set may still include words that interfere with learning in encoder-decoder models. This paper proposes a method for selecting more suitable words for learning encoders by utilizing not only frequency, but also co-occurrence information, which we capture using the HITS algorithm. The proposed method is applied to two tasks: machine translation and grammatical error correction. For Japanese-to-English translation, this method achieved a BLEU score that was 0.56 points more than that of a baseline. It also outperformed the baseline method for English grammatical error correction, with an F-measure that was 1.48 points higher.</abstract>
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%0 Conference Proceedings
%T Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models
%A Katsumata, Satoru
%A Matsumura, Yukio
%A Yamagishi, Hayahide
%A Komachi, Mamoru
%Y Shwartz, Vered
%Y Tabassum, Jeniya
%Y Voigt, Rob
%Y Che, Wanxiang
%Y de Marneffe, Marie-Catherine
%Y Nissim, Malvina
%S Proceedings of ACL 2018, Student Research Workshop
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F katsumata-etal-2018-graph
%X Encoder-decoder models typically only employ words that are frequently used in the training corpus because of the computational costs and/or to exclude noisy words. However, this vocabulary set may still include words that interfere with learning in encoder-decoder models. This paper proposes a method for selecting more suitable words for learning encoders by utilizing not only frequency, but also co-occurrence information, which we capture using the HITS algorithm. The proposed method is applied to two tasks: machine translation and grammatical error correction. For Japanese-to-English translation, this method achieved a BLEU score that was 0.56 points more than that of a baseline. It also outperformed the baseline method for English grammatical error correction, with an F-measure that was 1.48 points higher.
%R 10.18653/v1/P18-3016
%U https://aclanthology.org/P18-3016
%U https://doi.org/10.18653/v1/P18-3016
%P 112-119

Markdown (Informal)

[Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models](https://aclanthology.org/P18-3016) (Katsumata et al., ACL 2018)

• Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models (Katsumata et al., ACL 2018)

ACL

• Satoru Katsumata, Yukio Matsumura, Hayahide Yamagishi, and Mamoru Komachi. 2018. Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models. In Proceedings of ACL 2018, Student Research Workshop, pages 112–119, Melbourne, Australia. Association for Computational Linguistics.