  In e-commerce portals, generating answers for product-related questions has
become a crucial task. In this paper, 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 pose significant challenges to text generation tasks, and
e-commerce question-answering task is no exception. To generate more meaningful
answers, in this paper, 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.
