Affiliation:
1. Peking University
2. Shandong University
3. Renmin University of China and Peking University
Abstract
In e-commerce portals, generating answers for product-related questions has become a crucial task. In this article, we focus on the task of
product-aware answer generation
, which learns to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes.
However,
safe answer problems
(i.e., neural models tend to generate meaningless and universal answers) pose significant challenges to text generation tasks, and e-commerce question-answering task is no exception. To generate more meaningful answers, in this article, we propose a novel generative neural model, called the
Meaningful Product Answer Generator
(
MPAG
), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration. Product reviews and product attributes are used to provide meaningful content, while the prototype answer can yield a more diverse answer pattern. To this end, we propose a novel answer generator with a review reasoning module and a prototype answer reader. Our key idea is to obtain the correct question-aware information from a large-scale collection of reviews and learn how to write a coherent and meaningful answer from an existing prototype answer. To be more specific, we propose a read-and-write memory consisting of selective writing units to conduct
reasoning among these reviews
. We then employ a prototype reader consisting of comprehensive matching to extract the
answer skeleton
from the prototype answer. Finally, we propose an answer editor to generate the final answer by taking the question and the above parts as input. Conducted on a real-world dataset collected from an e-commerce platform, extensive experimental results show that our model achieves state-of-the-art performance in terms of both automatic metrics and human evaluations. Human evaluation also demonstrates that our model can consistently generate specific and proper answers.
Funder
Beijing Outstanding Young Scientist Program
Young Fellow of Beijing Institute of Artificial Intelligence
National Key R8D Program of China
National Science Foundation of China NSFC
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Science Applications,General Business, Management and Accounting,Information Systems
Cited by
16 articles.
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