Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
10-2020
Abstract
Product-related question answering (QA) is an important but challenging task in E-Commerce. It leads to a great demand on automatic review-driven QA, which aims at providing instant responses towards user-posted questions based on diverse product reviews. Nevertheless, the rich information about personal opinions in product reviews, which is essential to answer those product-specific questions, is underutilized in current generation-based review-driven QA studies. There are two main challenges when exploiting the opinion information from the reviews to facilitate the opinion-aware answer generation: (i) jointly modeling opinionated and interrelated information between the question and reviews to capture important information for answer generation, (ii) aggregating diverse opinion information to uncover the common opinion towards the given question. In this paper, we tackle opinion-aware answer generation by jointly learning answer generation and opinion mining tasks with a unified model. Two kinds of opinion fusion strategies, namely, static and dynamic fusion, are proposed to distill and aggregate important opinion information learned from the opinion mining task into the answer generation process. Then a multi-view pointer-generator network is employed to generate opinion-aware answers for a given product-related question. Experimental results show that our method achieves superior performance in real-world E-Commerce QA datasets, and effectively generate opinionated and informative answers.
Keywords
E-Commerce, question answering, review-driven answer generation, opinion mining
Discipline
Databases and Information Systems | E-Commerce
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, Virtual Conference, October 19-23
First Page
255
Last Page
264
ISBN
9781450368599
Identifier
10.1145/3340531.3411904
Publisher
ACM
City or Country
New York
Citation
DENG, Yang; ZHANG, Wenxuan; and LAM, Wai.
Opinion-aware answer generation for review-driven question answering in e-commerce. (2020). CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, Virtual Conference, October 19-23. 255-264.
Available at: https://ink.library.smu.edu.sg/sis_research/9109
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3340531.3411904