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

• URL: http://arxiv.org/abs/2509.05716v1
• Date: Sat, 06 Sep 2025 13:38:03 GMT
• Title: A Survey of the State-of-the-Art in Conversational Question Answering Systems
• Authors: Manoj Madushanka Perera, Adnan Mahmood, Kasun Eranda Wijethilake, Fahmida Islam, Maryam Tahermazandarani, Quan Z. Sheng,
• Abstract summary: Conversational Question Answering (ConvQA) systems have emerged as a pivotal area within Natural Language Processing (NLP)<n>This survey provides a comprehensive analysis of the state-of-the-art in ConvQA.
• Score: 12.629718069426112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• A Comprehensive Survey of Knowledge-Based Vision Question Answering Systems: The Lifecycle of Knowledge in Visual Reasoning Task [15.932332484902103]
  Knowledge-based Vision Question Answering (KB-VQA) extends general Vision Question Answering (VQA)<n>No comprehensive survey currently exists that systematically organizes and reviews the existing KB-VQA methods.
  arXiv  Detail & Related papers  (2025-04-24T13:37:25Z)
• The Quest for Visual Understanding: A Journey Through the Evolution of Visual Question Answering [17.43904098033175]
  Visual Question Answering (VQA) is an interdisciplinary field that bridges the gap between computer vision (CV) and natural language processing (NLP)<n>Since its inception in 2015, VQA has rapidly evolved, driven by advances in deep learning, attention mechanisms, and transformer-based models.<n>This survey traces the journey of VQA from its early days, through major breakthroughs, such as attention mechanisms, compositional reasoning, and the rise of vision-language pre-training methods.
  arXiv  Detail & Related papers  (2025-01-13T07:43:33Z)
• Visual question answering: from early developments to recent advances -- a survey [11.729464930866483]
  Visual Question Answering (VQA) is an evolving research field aimed at enabling machines to answer questions about visual content.<n>VQA has gained significant attention due to its broad applications, including interactive educational tools, medical image diagnosis, customer service, entertainment, and social media captioning.
  arXiv  Detail & Related papers  (2025-01-07T17:00:35Z)
• The Imperative of Conversation Analysis in the Era of LLMs: A Survey of Tasks, Techniques, and Trends [64.99423243200296]
  Conversation Analysis (CA) strives to uncover and analyze critical information from conversation data. In this paper, we perform a thorough review and systematize CA task to summarize the existing related work. We derive four key steps of CA from conversation scene reconstruction, to in-depth attribution analysis, and then to performing targeted training, finally generating conversations.
  arXiv  Detail & Related papers  (2024-09-21T16:52:43Z)
• FCC: Fusing Conversation History and Candidate Provenance for Contextual Response Ranking in Dialogue Systems [53.89014188309486]
  We present a flexible neural framework that can integrate contextual information from multiple channels. We evaluate our model on the MSDialog dataset widely used for evaluating conversational response ranking tasks.
  arXiv  Detail & Related papers  (2023-03-31T23:58:28Z)
• Achieving Human Parity on Visual Question Answering [67.22500027651509]
  The Visual Question Answering (VQA) task utilizes both visual image and language analysis to answer a textual question with respect to an image. This paper describes our recent research of AliceMind-MMU that obtains similar or even slightly better results than human beings does on VQA. This is achieved by systematically improving the VQA pipeline including: (1) pre-training with comprehensive visual and textual feature representation; (2) effective cross-modal interaction with learning to attend; and (3) A novel knowledge mining framework with specialized expert modules for the complex VQA task.
  arXiv  Detail & Related papers  (2021-11-17T04:25:11Z)
• Conversational Question Answering: A Survey [18.447856993867788]
  This survey is an effort to present a comprehensive review of the state-of-the-art research trends of Conversational Question Answering (CQA) Our findings show that there has been a trend shift from single-turn to multi-turn QA which empowers the field of Conversational AI from different perspectives.
  arXiv  Detail & Related papers  (2021-06-02T01:06:34Z)
• QAConv: Question Answering on Informative Conversations [85.2923607672282]
  We focus on informative conversations including business emails, panel discussions, and work channels. In total, we collect 34,204 QA pairs, including span-based, free-form, and unanswerable questions.
  arXiv  Detail & Related papers  (2021-05-14T15:53:05Z)
• BERT-CoQAC: BERT-based Conversational Question Answering in Context [10.811729691130349]
  We introduce a framework based on a publically available pre-trained language model called BERT for incorporating history turns into the system. Experiment results revealed that our framework is comparable in performance with the state-of-the-art models on the QuAC leader board.
  arXiv  Detail & Related papers  (2021-04-23T03:05:17Z)
• Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering [62.88322725956294]
  We review the latest research trends in OpenQA, with particular attention to systems that incorporate neural MRC techniques. We introduce modern OpenQA architecture named Retriever-Reader'' and analyze the various systems that follow this architecture. We then discuss key challenges to developing OpenQA systems and offer an analysis of benchmarks that are commonly used.
  arXiv  Detail & Related papers  (2021-01-04T04:47:46Z)
• A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges [71.4531144086568]
  Question Answering (QA) over Knowledge Base (KB) aims to automatically answer natural language questions. Researchers have shifted their attention from simple questions to complex questions, which require more KB triples and constraint inference.
  arXiv  Detail & Related papers  (2020-07-26T07:13:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.