Leveraging Structured Metadata for Improving Question Answering on the Web

Xinya Du, Ahmed Hassan Awadallah, Adam Fourney, Robert Sim, Paul Bennett, Claire Cardie


Abstract
We show that leveraging metadata information from web pages can improve the performance of models for answer passage selection/reranking. We propose a neural passage selection model that leverages metadata information with a fine-grained encoding strategy, which learns the representation for metadata predicates in a hierarchical way. The models are evaluated on the MS MARCO (Nguyen et al., 2016) and Recipe-MARCO datasets. Results show that our models significantly outperform baseline models, which do not incorporate metadata. We also show that the fine-grained encoding’s advantage over other strategies for encoding the metadata.
Anthology ID:
2020.aacl-main.55
Volume:
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
Month:
December
Year:
2020
Address:
Suzhou, China
Editors:
Kam-Fai Wong, Kevin Knight, Hua Wu
Venue:
AACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
551–556
Language:
URL:
https://aclanthology.org/2020.aacl-main.55
DOI:
Bibkey:
Cite (ACL):
Xinya Du, Ahmed Hassan Awadallah, Adam Fourney, Robert Sim, Paul Bennett, and Claire Cardie. 2020. Leveraging Structured Metadata for Improving Question Answering on the Web. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 551–556, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Leveraging Structured Metadata for Improving Question Answering on the Web (Du et al., AACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.aacl-main.55.pdf
Data
MS MARCO