Review-based Question Generation with Adaptive Instance Transfer and Augmentation

2019/11/05 by Qian Yu, Lidong Bing, Yu, Qian +7
Computer Science · #Computation and Language (cs.CL) #Expert finding and Q&A systems #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Multimodal Machine Learning Applications #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1911.01556

openalex publication_date 2019/11/05 · openalex created_date 2019/11/22 · openalex updated_date 2026/07/28

Abstract

Online reviews provide rich information about products and service, while it remains inefficient for potential consumers to exploit the reviews for fulfilling their specific information need. We propose to explore question generation as a new way of exploiting review information. One major challenge of this task is the lack of review-question pairs for training a neural generation model. We propose an iterative learning framework for handling this challenge via adaptive transfer and augmentation of the training instances with the help of the available user-posed question-answer data. To capture the aspect characteristics in reviews, the augmentation and generation procedures incorporate related features extracted via unsupervised learning. Experiments on data from 10 categories of a popular E-commerce site demonstrate the effectiveness of the framework, as well as the usefulness of the new task.

Citations

• LSTM-based Deep Learning Models for Non-factoid Answer Selection
• Question Answering and Question Generation as Dual Tasks

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