mlsanityData to 7 Oct 2026

Question rewriting? Assessing its importance for conversational question answering

arXiv 2201.09146 ↗First posted 22 Jan 2022cs.CL cs.LGcited by 15

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Abstract (arXiv)

In conversational question answering, systems must correctly interpret the interconnected interactions and generate knowledgeable answers, which may require the retrieval of relevant information from a background repository. Recent approaches to this problem leverage neural language models, although different alternatives can be considered in terms of modules for (a) representing user questions in context, (b) retrieving the relevant background information, and (c) generating the answer. This work presents a conversational question answering system designed specifically for the Search-Oriented Conversational AI (SCAI) shared task, and reports on a detailed analysis of its question rewriting module. In particular, we considered different variations of the question rewriting module to evaluate the influence on the subsequent components, and performed a careful analysis of the results obtained with the best system configuration. Our system achieved the best performance in the shared task and our analysis emphasizes the importance of the conversation context representation for the overall system performance.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 7 of its 13 references that are in the map. First posted on arXiv 22 Jan 2022.

Builds on

7 of 13 references.

References · 7

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  3. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  4. 2018QuAC : Question Answering in Contextcited by 379
  5. 2020Open-Domain Question Answering Goes Conversational via Question Rewritingcited by 147
  6. 2020Question Rewriting for Conversational Question Answeringcited by 93
  7. 2021A Comparison of Question Rewriting Methods for Conversational Passage Retrievalcited by 16

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Citing papers per year: 2022: 6; 2023: 5; 2024: 1; 2025: 3.

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  1. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 316
  2. 2024ChatQA: Surpassing GPT-4 on Conversational QA and RAGcited by 90
  3. 2022PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Financecited by 45
  4. 2022Conversational Question Answering on Heterogeneous Sourcescited by 26
  5. 2022Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Surveycited by 14
  6. 2023Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generationcited by 7
  7. 2022SCAI-QReCC Shared Task on Conversational Question Answeringcited by 4
  8. 2023Learning to Select the Relevant History Turns in Conversational Question Answeringcited by 4
  9. 2023Keeping the Questions Conversational: Using Structured Representations to Resolve Dependency in Conversational Question Answeringcited by 3
  10. 2025Towards Adaptive Context Management for Intelligent Conversational Question Answeringcited by 3
  11. 2022Query Enhanced Knowledge-Intensive Conversation via Unsupervised Joint Modelingcited by 2
  12. 2023Realistic Conversational Question Answering with Answer Selection based on Calibrated Confidence and Uncertainty Measurementcited by 2
3 more
  1. 2023Phrase Retrieval for Open-Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learningcited by 2
  2. 2025A Survey of the State-of-the-Art in Conversational Question Answering Systemscited by 1
  3. 2025SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphscited by 1