Addressee and Response Selection for Multilingual Conversation

Motoki Sato, Hiroki Ouchi, Yuta Tsuboi


Abstract
Developing conversational systems that can converse in many languages is an interesting challenge for natural language processing. In this paper, we introduce multilingual addressee and response selection. In this task, a conversational system predicts an appropriate addressee and response for an input message in multiple languages. A key to developing such multilingual responding systems is how to utilize high-resource language data to compensate for low-resource language data. We present several knowledge transfer methods for conversational systems. To evaluate our methods, we create a new multilingual conversation dataset. Experiments on the dataset demonstrate the effectiveness of our methods.
Anthology ID:
C18-1308
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3631–3644
Language:
URL:
https://aclanthology.org/C18-1308
DOI:
Bibkey:
Cite (ACL):
Motoki Sato, Hiroki Ouchi, and Yuta Tsuboi. 2018. Addressee and Response Selection for Multilingual Conversation. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3631–3644, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Addressee and Response Selection for Multilingual Conversation (Sato et al., COLING 2018)
Copy Citation:
PDF:
https://aclanthology.org/C18-1308.pdf
Code
 aonotas/multilingual_ASR