Contextualized Sparse Representations for Real-Time Open-Domain Question Answering

Jinhyuk Lee, Minjoon Seo, Hannaneh Hajishirzi, Jaewoo Kang


Abstract
Open-domain question answering can be formulated as a phrase retrieval problem, in which we can expect huge scalability and speed benefit but often suffer from low accuracy due to the limitation of existing phrase representation models. In this paper, we aim to improve the quality of each phrase embedding by augmenting it with a contextualized sparse representation (Sparc). Unlike previous sparse vectors that are term-frequency-based (e.g., tf-idf) or directly learned (only few thousand dimensions), we leverage rectified self-attention to indirectly learn sparse vectors in n-gram vocabulary space. By augmenting the previous phrase retrieval model (Seo et al., 2019) with Sparc, we show 4%+ improvement in CuratedTREC and SQuAD-Open. Our CuratedTREC score is even better than the best known retrieve & read model with at least 45x faster inference speed.
Anthology ID:
2020.acl-main.85
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
912–919
Language:
URL:
https://aclanthology.org/2020.acl-main.85
DOI:
10.18653/v1/2020.acl-main.85
Bibkey:
Cite (ACL):
Jinhyuk Lee, Minjoon Seo, Hannaneh Hajishirzi, and Jaewoo Kang. 2020. Contextualized Sparse Representations for Real-Time Open-Domain Question Answering. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 912–919, Online. Association for Computational Linguistics.
Cite (Informal):
Contextualized Sparse Representations for Real-Time Open-Domain Question Answering (Lee et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.85.pdf
Video:
 http://slideslive.com/38929083
Code
 jhyuklee/sparc +  additional community code
Data
SQuAD