Sentiment enhanced answer generation and information fusing for product-related question answering

Abstract

The reviews written by users in E-commerce platform have been fully exploited by product-related question answering systems, which ignore the product descriptions with valuable information to improve the system performance, especially for objective questions. In this paper, we propose a model named as Sentiment-enhanced Answer Generation and product Descriptions Fusing (SAGDF) for Product-related Question Answering task. The valid information from user reviews and product descriptions are learned jointly for answer generation. Importantly, the sentiment analysis task is performed initially to make the model judge the sentiment tendency correctly for answer accuracy. The review encoder of the model is fine-tuned on the large-scale Amazon review dataset to implement the sentiment analysis task. In addition, we build the dataset named AmazonQARD and it contains both reviews and corresponding product descriptions. The proposed model is trained on AmazonQARD and it processes user reviews individually and integrates product descriptions to constantly correct the generated answers iteratively. Experimental results demonstrate that SAGDF significantly outperforms the other baseline systems.