Modeling Target-side Inflection in Placeholder Translation

Ryokan Ri, Toshiaki Nakazawa, Yoshimasa Tsuruoka


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.
Anthology ID:
2021.mtsummit-research.19
Volume:
Proceedings of Machine Translation Summit XVIII: Research Track
Month:
August
Year:
2021
Address:
Virtual
Editors:
Kevin Duh, Francisco Guzmán
Venue:
MTSummit
SIG:
Publisher:
Association for Machine Translation in the Americas
Note:
Pages:
231–242
Language:
URL:
https://aclanthology.org/2021.mtsummit-research.19
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Modeling Target-side Inflection in Placeholder Translation (Ri et al., MTSummit 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.mtsummit-research.19.pdf
Code
 Ryou0634/placeholder_translation
Data
ASPEC