Answering Product-related Questions with Heterogeneous Information

Wenxuan Zhang, Qian Yu, Wai Lam

Abstract
Providing instant response for product-related questions in E-commerce question answering platforms can greatly improve users’ online shopping experience. However, existing product question answering (PQA) methods only consider a single information source such as user reviews and/or require large amounts of labeled data. In this paper, we propose a novel framework to tackle the PQA task via exploiting heterogeneous information including natural language text and attribute-value pairs from two information sources of the concerned product, namely product details and user reviews. A heterogeneous information encoding component is then designed for obtaining unified representations of information with different formats. The sources of the candidate snippets are also incorporated when measuring the question-snippet relevance. Moreover, the framework is trained with a specifically designed weak supervision paradigm making use of available answers in the training phase. Experiments on a real-world dataset show that our proposed framework achieves superior performance over state-of-the-art models.
Anthology ID:
2020.aacl-main.70
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:
696–705
Language:
URL:
https://aclanthology.org/2020.aacl-main.70
DOI:
Bibkey:
Cite (ACL):
Wenxuan Zhang, Qian Yu, and Wai Lam. 2020. Answering Product-related Questions with Heterogeneous Information. 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 696–705, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Answering Product-related Questions with Heterogeneous Information (Zhang et al., AACL 2020)
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