Review-Driven Answer Generation for Product-Related Questions in E-Commerce
This addresses the time-consuming task for users of sifting through reviews to find product information in e-commerce, though it is incremental as it builds on existing generation models with a novel mechanism.
The paper tackles the problem of generating answers to product-related questions in e-commerce by proposing RAGE, a review-driven framework that extracts and incorporates relevant information from noisy reviews, resulting in significantly more accurate and informative answers and faster training and generation times compared to existing models.
The users often have many product-related questions before they make a purchase decision in E-commerce. However, it is often time-consuming to examine each user review to identify the desired information. In this paper, we propose a novel review-driven framework for answer generation for product-related questions in E-commerce, named RAGE. We develope RAGE on the basis of the multi-layer convolutional architecture to facilitate speed-up of answer generation with the parallel computation. For each question, RAGE first extracts the relevant review snippets from the reviews of the corresponding product. Then, we devise a mechanism to identify the relevant information from the noise-prone review snippets and incorporate this information to guide the answer generation. The experiments on two real-world E-Commerce datasets show that the proposed RAGE significantly outperforms the existing alternatives in producing more accurate and informative answers in natural language. Moreover, RAGE takes much less time for both model training and answer generation than the existing RNN based generation models.