A Survey of the State-of-the-Art in Conversational Question Answering Systems

2025-09-06

AI Summary

Conversational Question Answering (ConvQA) faces core challenges including weak inter-turn coherence and poor cross-domain generalization. This paper systematically surveys recent advances in ConvQA, focusing on three key mechanisms: historical information selection, context-aware question understanding, and answer generation. Methodologically, it innovatively integrates reinforcement learning, contrastive learning, and transfer learning paradigms; empirically evaluates the roles of large language models—including GPT-4, LLaMA-3, Gemini 2.0 Flash, and Mistral 7B—in architectural design and data augmentation; and enhances dialogue state modeling and fine-grained semantic understanding via RoBERTa-based optimization. The study unifies major benchmarks (e.g., QuAC, CoQA, Doc2Dial) and evaluation metrics, identifies critical bottlenecks—such as long-range dependency modeling and inefficient domain adaptation—and proposes scalable research directions: context compression and instruction alignment. These contributions establish a theoretical framework and practical guidelines for developing robust, cross-domain ConvQA systems. (149 words)

Technology Category

• Natural Language Processing: Question Answering
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Community question answering

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.

Problem

• Surveying state-of-the-art conversational question answering systems
• Analyzing core components like history selection and answer prediction
• Exploring machine learning techniques and large language models impact

Innovation

• Advanced machine learning techniques for accuracy
• Large language models for data scalability
• Core components ensuring conversation coherence relevance