AugSBertChat: User Feedback-Enhanced QA with Sentence-RoBERTa

Bin Zhang, Tingting Zhao, Chong Ma, Yarui Chen, Yuan Wang

Abstract: Generative Question Answering (GQA) has spread across various industries. However, the potential of GQA during Question Answering (QA) interactions remains underutilized. Consequently, this paper introduces AugSBertChat, a GQA model that integrates user feedback for enhanced performance and utility. This method can be divided into two parts: predicting the probability of liking a reply and generating the reply. In order to make better use of user feedback to improve the quality of replies, we first formulate the QA task as the Semantic Text Similarity (STS) task, using Sentence-RoBERTa to obtain the similarity of QA pairs in high-dimensional space. In particular, in-domain symmetric semantic search is used to enhance our model performance. Subsequently, we construct some prompts that are more suitable for XiaoAi’s QA scene, and employ P-tuning v2 to efficiently fine-tune the ChatGLM-6B parameters. Finally, we conducted experiments in the NLPCC-2023-Shared-Task-9 User Feedback Prediction and Response Generation (UFPRG) and achieved good results, placing third among all teams, which demonstrates the effectiveness of our proposed method.