Abstract
Conversational Question Answering (ConvQA) systems have emerged as a pivotal area within Natural Language Processing (NLP) by driving advancements that enable machines to engage in dynamic and context-aware conversations. These capabilities are increasingly being applied across various domains, i.e., customer support, education, legal, and healthcare where maintaining a coherent and relevant conversation is essential. Building on recent advancements, this survey provides a comprehensive analysis of the state-of-the-art in ConvQA. This survey begins by examining the core components of ConvQA systems, i.e., history selection, question understanding, and answer prediction, highlighting their interplay in ensuring coherence and relevance in multi-turn conversations. It further investigates the use of advanced machine learning techniques, including but not limited to, reinforcement learning, contrastive learning, and transfer learning to improve ConvQA accuracy and efficiency. The pivotal role of large language models, i.e., RoBERTa, GPT-4, Gemini 2.0 Flash, Mistral 7B, and LLaMA 3, is also explored, thereby showcasing their impact through data scalability and architectural advancements. Additionally, this survey presents a comprehensive analysis of key ConvQA datasets and concludes by outlining open research directions. Overall, this work offers a comprehensive overview of the ConvQA landscape and provides valuable insights to guide future advancements in the field.
Abstract (translated)
对话式问答（ConvQA）系统在自然语言处理（NLP）领域中已成为一个至关重要的研究方向，推动了机器进行动态和上下文感知的会话能力的发展。这种技术正在被广泛应用于各个领域，例如客户服务、教育、法律及医疗保健等，这些领域需要维持连贯且相关性的对话内容。基于近期的研究进展，本文综述提供了关于ConvQA最新技术水平的全面分析。 此综述首先审视了ConvQA系统的核心组成部分——历史信息选择、问题理解和答案预测，并强调它们在确保多轮会话中连贯性和相关性方面的相互作用。此外，还探讨了包括强化学习、对比学习和迁移学习在内的高级机器学习技术的应用，以提高ConvQA的准确性和效率。综述还探索了大型语言模型（如RoBERTa, GPT-4, Gemini 2.0 Flash, Mistral 7B 和 LLaMA 3）在其数据规模扩展及架构改进方面所发挥的关键作用，并展示了它们在ConvQA中的影响。此外，本文还对关键的ConvQA数据集进行了全面分析，并总结了当前的研究方向。 总的来说，这项工作提供了关于ConvQA领域现状的一个全面概述，并为未来该领域的进一步发展提供了宝贵的见解和指导。
URL
https://arxiv.org/abs/2509.05716
