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        <title>Japanese Text Normalization with Encoder-Decoder Model</title>
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    <name type="personal">
        <namePart type="given">Taishi</namePart>
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        <namePart type="family">Shindo</namePart>
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            <title>Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)</title>
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            <namePart type="given">Bo</namePart>
            <namePart type="family">Han</namePart>
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    <abstract>Text normalization is the task of transforming lexical variants to their canonical forms. We model the problem of text normalization as a character-level sequence to sequence learning problem and present a neural encoder-decoder model for solving it. To train the encoder-decoder model, many sentences pairs are generally required. However, Japanese non-standard canonical pairs are scarce in the form of parallel corpora. To address this issue, we propose a method of data augmentation to increase data size by converting existing resources into synthesized non-standard forms using handcrafted rules. We conducted an experiment to demonstrate that the synthesized corpus contributes to stably train an encoder-decoder model and improve the performance of Japanese text normalization.</abstract>
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%0 Conference Proceedings
%T Japanese Text Normalization with Encoder-Decoder Model
%A Ikeda, Taishi
%A Shindo, Hiroyuki
%A Matsumoto, Yuji
%Y Han, Bo
%Y Ritter, Alan
%Y Derczynski, Leon
%Y Xu, Wei
%Y Baldwin, Tim
%S Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F ikeda-etal-2016-japanese
%X Text normalization is the task of transforming lexical variants to their canonical forms. We model the problem of text normalization as a character-level sequence to sequence learning problem and present a neural encoder-decoder model for solving it. To train the encoder-decoder model, many sentences pairs are generally required. However, Japanese non-standard canonical pairs are scarce in the form of parallel corpora. To address this issue, we propose a method of data augmentation to increase data size by converting existing resources into synthesized non-standard forms using handcrafted rules. We conducted an experiment to demonstrate that the synthesized corpus contributes to stably train an encoder-decoder model and improve the performance of Japanese text normalization.
%U https://aclanthology.org/W16-3918
%P 129-137

Markdown (Informal)

[Japanese Text Normalization with Encoder-Decoder Model](https://aclanthology.org/W16-3918) (Ikeda et al., WNUT 2016)

• Japanese Text Normalization with Encoder-Decoder Model (Ikeda et al., WNUT 2016)

ACL

• Taishi Ikeda, Hiroyuki Shindo, and Yuji Matsumoto. 2016. Japanese Text Normalization with Encoder-Decoder Model. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 129–137, Osaka, Japan. The COLING 2016 Organizing Committee.