Distantly Supervised Transformers For E-Commerce Product QA

Happy Mittal, Aniket Chakrabarti, Belhassen Bayar, Animesh Anant Sharma, Nikhil Rasiwasia


Abstract
We propose a practical instant question answering (QA) system on product pages of e-commerce services, where for each user query, relevant community question answer (CQA) pairs are retrieved. User queries and CQA pairs differ significantly in language characteristics making relevance learning difficult. Our proposed transformer-based model learns a robust relevance function by jointly learning unified syntactic and semantic representations without the need for human labeled data. This is achieved by distantly supervising our model by distilling from predictions of a syntactic matching system on user queries and simultaneously training with CQA pairs. Training with CQA pairs helps our model learning semantic QA relevance and distant supervision enables learning of syntactic features as well as the nuances of user querying language. Additionally, our model encodes queries and candidate responses independently allowing offline candidate embedding generation thereby minimizing the need for real-time transformer model execution. Consequently, our framework is able to scale to large e-commerce QA traffic. Extensive evaluation on user queries shows that our framework significantly outperforms both syntactic and semantic baselines in offline as well as large scale online A/B setups of a popular e-commerce service.
Anthology ID:
2021.naacl-main.315
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Editors:
Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4008–4017
Language:
URL:
https://aclanthology.org/2021.naacl-main.315
DOI:
10.18653/v1/2021.naacl-main.315
Bibkey:
Cite (ACL):
Happy Mittal, Aniket Chakrabarti, Belhassen Bayar, Animesh Anant Sharma, and Nikhil Rasiwasia. 2021. Distantly Supervised Transformers For E-Commerce Product QA. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4008–4017, Online. Association for Computational Linguistics.
Cite (Informal):
Distantly Supervised Transformers For E-Commerce Product QA (Mittal et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.315.pdf
Video:
 https://aclanthology.org/2021.naacl-main.315.mp4