Less Is More: Rejecting Unreliable Reviews for Product Question Answering
Shiwei Zhang, Xiuzhen Zhang, Jey Han Lau, Jeffrey Chan, Cecile Paris, F Hutter (ed.), K Kersting (ed.), J Lijffijt (ed.), I Valera (ed.)
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2020, PT III | SPRINGER INTERNATIONAL PUBLISHING AG | Published : 2021
Promptly and accurately answering questions on products is important for e-commerce applications. Manually answering product questions (e.g. on community question answering platforms) results in slow response and does not scale. Recent studies show that product reviews are a good source for real-time, automatic product question answering (PQA). In the literature, PQA is formulated as a retrieval problem with the goal to search for the most relevant reviews to answer a given product question. In this paper, we focus on the issue of answerability and answer reliability for PQA using reviews. Our investigation is based on the intuition that many questions may not be answerable with a finite set..View full abstract
Shiwei Zhang is supported by the RMIT University and CSIRO Data61 Scholarships.