%0 Conference Proceedings %T Answering Product-related Questions with Heterogeneous Information %A Zhang, Wenxuan %A Yu, Qian %A Lam, Wai %Y Wong, Kam-Fai %Y Knight, Kevin %Y Wu, Hua %S 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 %D 2020 %8 December %I Association for Computational Linguistics %C Suzhou, China %F zhang-etal-2020-answering %X 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. %U https://aclanthology.org/2020.aacl-main.70 %P 696-705