@inproceedings{liang-etal-2021-towards,
title = "Towards Making the Most of Dialogue Characteristics for Neural Chat Translation",
author = "Liang, Yunlong and
Zhou, Chulun and
Meng, Fandong and
Xu, Jinan and
Chen, Yufeng and
Su, Jinsong and
Zhou, Jie",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.6",
doi = "10.18653/v1/2021.emnlp-main.6",
pages = "67--79",
abstract = "Neural Chat Translation (NCT) aims to translate conversational text between speakers of different languages. Despite the promising performance of sentence-level and context-aware neural machine translation models, there still remain limitations in current NCT models because the inherent dialogue characteristics of chat, such as dialogue coherence and speaker personality, are neglected. In this paper, we propose to promote the chat translation by introducing the modeling of dialogue characteristics into the NCT model. To this end, we design four auxiliary tasks including monolingual response generation, cross-lingual response generation, next utterance discrimination, and speaker identification. Together with the main chat translation task, we optimize the enhanced NCT model through the training objectives of all these tasks. By this means, the NCT model can be enhanced by capturing the inherent dialogue characteristics, thus generating more coherent and speaker-relevant translations. Comprehensive experiments on four language directions (English{\textless}-{\textgreater}German and English{\textless}-{\textgreater}Chinese) verify the effectiveness and superiority of the proposed approach.",
}
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<abstract>Neural Chat Translation (NCT) aims to translate conversational text between speakers of different languages. Despite the promising performance of sentence-level and context-aware neural machine translation models, there still remain limitations in current NCT models because the inherent dialogue characteristics of chat, such as dialogue coherence and speaker personality, are neglected. In this paper, we propose to promote the chat translation by introducing the modeling of dialogue characteristics into the NCT model. To this end, we design four auxiliary tasks including monolingual response generation, cross-lingual response generation, next utterance discrimination, and speaker identification. Together with the main chat translation task, we optimize the enhanced NCT model through the training objectives of all these tasks. By this means, the NCT model can be enhanced by capturing the inherent dialogue characteristics, thus generating more coherent and speaker-relevant translations. Comprehensive experiments on four language directions (English\textless-\textgreaterGerman and English\textless-\textgreaterChinese) verify the effectiveness and superiority of the proposed approach.</abstract>
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%0 Conference Proceedings
%T Towards Making the Most of Dialogue Characteristics for Neural Chat Translation
%A Liang, Yunlong
%A Zhou, Chulun
%A Meng, Fandong
%A Xu, Jinan
%A Chen, Yufeng
%A Su, Jinsong
%A Zhou, Jie
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F liang-etal-2021-towards
%X Neural Chat Translation (NCT) aims to translate conversational text between speakers of different languages. Despite the promising performance of sentence-level and context-aware neural machine translation models, there still remain limitations in current NCT models because the inherent dialogue characteristics of chat, such as dialogue coherence and speaker personality, are neglected. In this paper, we propose to promote the chat translation by introducing the modeling of dialogue characteristics into the NCT model. To this end, we design four auxiliary tasks including monolingual response generation, cross-lingual response generation, next utterance discrimination, and speaker identification. Together with the main chat translation task, we optimize the enhanced NCT model through the training objectives of all these tasks. By this means, the NCT model can be enhanced by capturing the inherent dialogue characteristics, thus generating more coherent and speaker-relevant translations. Comprehensive experiments on four language directions (English\textless-\textgreaterGerman and English\textless-\textgreaterChinese) verify the effectiveness and superiority of the proposed approach.
%R 10.18653/v1/2021.emnlp-main.6
%U https://aclanthology.org/2021.emnlp-main.6
%U https://doi.org/10.18653/v1/2021.emnlp-main.6
%P 67-79
Markdown (Informal)
[Towards Making the Most of Dialogue Characteristics for Neural Chat Translation](https://aclanthology.org/2021.emnlp-main.6) (Liang et al., EMNLP 2021)
ACL