TY - GEN
T1 - XDailyDialog
T2 - 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023
AU - Liu, Zeming
AU - Nie, Ping
AU - Cai, Jie
AU - Wang, Haifeng
AU - Niu, Zheng Yu
AU - Zhang, Peng
AU - Sachan, Mrinmaya
AU - Peng, Kaiping
N1 - Publisher Copyright:
© 2023 Association for Computational Linguistics.
PY - 2023
Y1 - 2023
N2 - High-quality corpora are significant to the development of dialogue models. However, most existing corpora for open-domain dialogue modeling are limited to a single language. The absence of multilingual open-domain dialog corpora not only limits the research on multilingual or cross-lingual transfer learning but also hinders the development of robust open-domain dialogue systems that can be deployed in other parts of the world. In this paper, we provide a multilingual parallel open-domain dialog dataset, XDailyDialog, to enable researchers to explore the challenging task of multilingual and cross-lingual open-domain dialogue. XDailyDialog includes 13K dialogues aligned across 4 languages (52K dialogues and 410K utterances in total). We then propose a dialogue generation model, kNN-Chat, which has a novel kNN-search mechanism to support unified response retrieval for monolingual, multilingual, and cross-lingual dialogue. Experiment results show the effectiveness of this framework.
AB - High-quality corpora are significant to the development of dialogue models. However, most existing corpora for open-domain dialogue modeling are limited to a single language. The absence of multilingual open-domain dialog corpora not only limits the research on multilingual or cross-lingual transfer learning but also hinders the development of robust open-domain dialogue systems that can be deployed in other parts of the world. In this paper, we provide a multilingual parallel open-domain dialog dataset, XDailyDialog, to enable researchers to explore the challenging task of multilingual and cross-lingual open-domain dialogue. XDailyDialog includes 13K dialogues aligned across 4 languages (52K dialogues and 410K utterances in total). We then propose a dialogue generation model, kNN-Chat, which has a novel kNN-search mechanism to support unified response retrieval for monolingual, multilingual, and cross-lingual dialogue. Experiment results show the effectiveness of this framework.
UR - https://www.scopus.com/pages/publications/85174423056
U2 - 10.18653/v1/2023.acl-long.684
DO - 10.18653/v1/2023.acl-long.684
M3 - 会议稿件
AN - SCOPUS:85174423056
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 12240
EP - 12253
BT - Long Papers
PB - Association for Computational Linguistics (ACL)
Y2 - 9 July 2023 through 14 July 2023
ER -