Language Scaling for Universal Suggested Replies Model

Qianlan Ying, Payal Bajaj, Budhaditya Deb, Yu Yang, Wei Wang, Bojia Lin, Milad Shokouhi, Xia Song, Yang Yang, Daxin Jiang


Abstract
We consider the problem of scaling automated suggested replies for a commercial email application to multiple languages. Faced with increased compute requirements and low language resources for language expansion, we build a single universal model for improving the quality and reducing run-time costs of our production system. However, restricted data movement across regional centers prevents joint training across languages. To this end, we propose a multi-lingual multi-task continual learning framework, with auxiliary tasks and language adapters to train universal language representation across regions. The experimental results show positive cross-lingual transfer across languages while reducing catastrophic forgetting across regions. Our online results on real user traffic show significant CTR and Char-saved gain as well as 65% training cost reduction compared with per-language models. As a consequence, we have scaled the feature in multiple languages including low-resource markets.
Anthology ID:
2021.naacl-industry.18
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
138–145
Language:
URL:
https://aclanthology.org/2021.naacl-industry.18
DOI:
10.18653/v1/2021.naacl-industry.18
Bibkey:
Cite (ACL):
Qianlan Ying, Payal Bajaj, Budhaditya Deb, Yu Yang, Wei Wang, Bojia Lin, Milad Shokouhi, Xia Song, Yang Yang, and Daxin Jiang. 2021. Language Scaling for Universal Suggested Replies Model. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers, pages 138–145, Online. Association for Computational Linguistics.
Cite (Informal):
Language Scaling for Universal Suggested Replies Model (Ying et al., NAACL 2021)
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