S2M: Converting Single-Turn to Multi-Turn Datasets for Conversational Question Answering

• DOI: 10.48550/arxiv.2312.16511
• Publication Date: 2023-09-28
• Authors: Li, Baokui; Zhang, Sen; Zhang, Wangshu; Chen, Yicheng; Yang, Changlin; Hu, Sen; Xu, Teng; liu, Siye; Li, Jiwei
• Keywords: FOS: Computer and information sciences; Computer Science - Computation and Language; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; Computation and Language (cs.CL)

Supplying data augmentation to conversational question answering (CQA) can effectively improve model performance. However, there is less improvement from single-turn datasets in CQA due to the distribution gap between single-turn and multi-turn datasets. On the other hand, while numerous single-turn datasets are available, we have not utilized them effectively. To solve this problem, we propose a novel method to convert single-turn datasets to multi-turn datasets. The proposed method consists of three parts, namely, a QA pair Generator, a QA pair Reassembler, and a question Rewriter. Given a sample consisting of context and single-turn QA pairs, the Generator obtains candidate QA pairs and a knowledge graph based on the context. The Reassembler utilizes the knowledge graph to get sequential QA pairs, and the Rewriter rewrites questions from a conversational perspective to obtain a multi-turn dataset S2M. Our experiments show that our method can synthesize effective training resources for CQA. Notably, S2M ranks 1st place on the QuAC leaderboard (https://quac.ai/) at the time of submission (Aug 24th, 2022).