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:
- 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)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2020.aacl-main.70.pdf