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.

本文提出了一种基于统一模型的答案生成方法，通过 opinion mining 解决 product-related question answering 中因个人意见多样性带来的挑战，实现 opinion-aware 答案生成，并通过多视角 pointer-generator 网络实现答案生成和意见挖掘任务之间的联合学习和意见融合策略，取得了在实际 E-Commerce QA 数据集中优越表现。