Opinion-aware Answer Generation for Review-driven Question Answering in
E-Commerce
- URL: http://arxiv.org/abs/2008.11972v2
- Date: Fri, 28 Aug 2020 05:29:47 GMT
- Title: Opinion-aware Answer Generation for Review-driven Question Answering in
E-Commerce
- Authors: Yang Deng, Wenxuan Zhang, Wai Lam
- Abstract summary: rich information about personal opinions in product reviews is underutilized in current generation-based review-driven QA studies.
In this paper, we tackle opinion-aware answer generation by jointly learning answer generation and opinion mining tasks with a unified model.
Experimental results show that our method achieves superior performance in real-world E-Commerce QA datasets.
- Score: 39.08269647808958
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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