Product-Aware Answer Generation in E-Commerce Question-Answering
arXiv:1901.07696
Abstract
In e-commerce portals, generating answers for product-related questions has become a crucial task. In this paper, we propose the task of product-aware answer generation, which tends to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. Unlike existing question-answering problems, answer generation in e-commerce confronts three main challenges: (1) Reviews are informal and noisy; (2) joint modeling of reviews and key-value product attributes is challenging; (3) traditional methods easily generate meaningless answers. To tackle above challenges, we propose an adversarial learning based model, named PAAG, which is composed of three components: a question-aware review representation module, a key-value memory network encoding attributes, and a recurrent neural network as a sequence generator. Specifically, we employ a convolutional discriminator to distinguish whether our generated answer matches the facts. To extract the salience part of reviews, an attention-based review reader is proposed to capture the most relevant words given the question. Conducted on a large-scale real-world e-commerce dataset, our extensive experiments verify the effectiveness of each module in our proposed model. Moreover, our experiments show that our model achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations.
Accepted by WSDM 2019
References in corpus (7)
- Improved Training of Wasserstein GANs
- SQuAD: 100,000+ Questions for Machine Comprehension of Text
- A Convolutional Neural Network for Modelling Sentences
- Incorporating Copying Mechanism in Sequence-to-Sequence Learning
- RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems
- S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension
- Query-Based Abstractive Summarization Using Neural Networks