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        <title>Modeling Target-side Inflection in Placeholder Translation</title>
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    <abstract>Placeholder translation systems enable the users to specify how a specific phrase is translated in the output sentence. The system is trained to output special placeholder tokens and the user-specified term is injected into the output through the context-free replacement of the placeholder token. However and this approach could result in ungrammatical sentences because it is often the case that the specified term needs to be inflected according to the context of the output and which is unknown before the translation. To address this problem and we propose a novel method of placeholder translation that can inflect specified terms according to the grammatical construction of the output sentence. We extend the seq2seq architecture with a character-level decoder that takes the lemma of a user-specified term and the words generated from the word-level decoder to output a correct inflected form of the lemma. We evaluate our approach with a Japanese-to-English translation task in the scientific writing domain and and show our model can incorporate specified terms in a correct form more successfully than other comparable models.</abstract>
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%0 Conference Proceedings
%T Modeling Target-side Inflection in Placeholder Translation
%A Ri, Ryokan
%A Nakazawa, Toshiaki
%A Tsuruoka, Yoshimasa
%Y Duh, Kevin
%Y Guzmán, Francisco
%S Proceedings of Machine Translation Summit XVIII: Research Track
%D 2021
%8 August
%I Association for Machine Translation in the Americas
%C Virtual
%F ri-etal-2021-modeling
%X Placeholder translation systems enable the users to specify how a specific phrase is translated in the output sentence. The system is trained to output special placeholder tokens and the user-specified term is injected into the output through the context-free replacement of the placeholder token. However and this approach could result in ungrammatical sentences because it is often the case that the specified term needs to be inflected according to the context of the output and which is unknown before the translation. To address this problem and we propose a novel method of placeholder translation that can inflect specified terms according to the grammatical construction of the output sentence. We extend the seq2seq architecture with a character-level decoder that takes the lemma of a user-specified term and the words generated from the word-level decoder to output a correct inflected form of the lemma. We evaluate our approach with a Japanese-to-English translation task in the scientific writing domain and and show our model can incorporate specified terms in a correct form more successfully than other comparable models.
%U https://aclanthology.org/2021.mtsummit-research.19
%P 231-242

Markdown (Informal)

[Modeling Target-side Inflection in Placeholder Translation](https://aclanthology.org/2021.mtsummit-research.19) (Ri et al., MTSummit 2021)

• Modeling Target-side Inflection in Placeholder Translation (Ri et al., MTSummit 2021)

ACL

• Ryokan Ri, Toshiaki Nakazawa, and Yoshimasa Tsuruoka. 2021. Modeling Target-side Inflection in Placeholder Translation. In Proceedings of Machine Translation Summit XVIII: Research Track, pages 231–242, Virtual. Association for Machine Translation in the Americas.