A Dataset and Baselines for Multilingual Reply Suggestion

Mozhi Zhang, Wei Wang, Budhaditya Deb, Guoqing Zheng, Milad Shokouhi, Ahmed Hassan Awadallah


Abstract
Reply suggestion models help users process emails and chats faster. Previous work only studies English reply suggestion. Instead, we present MRS, a multilingual reply suggestion dataset with ten languages. MRS can be used to compare two families of models: 1) retrieval models that select the reply from a fixed set and 2) generation models that produce the reply from scratch. Therefore, MRS complements existing cross-lingual generalization benchmarks that focus on classification and sequence labeling tasks. We build a generation model and a retrieval model as baselines for MRS. The two models have different strengths in the monolingual setting, and they require different strategies to generalize across languages. MRS is publicly available at https://github.com/zhangmozhi/mrs.
Anthology ID:
2021.acl-long.97
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1207–1220
Language:
URL:
https://aclanthology.org/2021.acl-long.97
DOI:
10.18653/v1/2021.acl-long.97
Bibkey:
Cite (ACL):
Mozhi Zhang, Wei Wang, Budhaditya Deb, Guoqing Zheng, Milad Shokouhi, and Ahmed Hassan Awadallah. 2021. A Dataset and Baselines for Multilingual Reply Suggestion. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1207–1220, Online. Association for Computational Linguistics.
Cite (Informal):
A Dataset and Baselines for Multilingual Reply Suggestion (Zhang et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.97.pdf
Video:
 https://aclanthology.org/2021.acl-long.97.mp4
Code
 zhangmozhi/mrs
Data
MRS