# S2M: Converting Single-Turn to Multi-Turn Datasets for Conversational Question Answering Source: [S2M: Converting Single-Turn to Multi-Turn Datasets for Conversational Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:28:17.403Z Authors: Baokui Li, Sen Zhang, Wangshu Zhang, Yicheng Chen, Changlin Yang, Sen Hu, Teng Xu, Siye liu, Jiwei Li Published: 2023-12-27T10:41:18.000Z ## Abstract 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 at the time of submission (Aug 24th, 2022). ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()