mlsanityData to 14 Oct 2026

From Rewriting to Remembering: Common Ground for Conversational QA Models

arXiv 2204.03930 ↗First posted 8 Apr 2022cs.CLcited by 5

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Abstract (arXiv)

In conversational QA, models have to leverage information in previous turns to answer upcoming questions. Current approaches, such as Question Rewriting, struggle to extract relevant information as the conversation unwinds. We introduce the Common Ground (CG), an approach to accumulate conversational information as it emerges and select the relevant information at every turn. We show that CG offers a more efficient and human-like way to exploit conversational information compared to existing approaches, leading to improvements on Open Domain Conversational QA.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 13 of its 17 references that are in the map. First posted on arXiv 8 Apr 2022.

Builds on

13 of 17 references.

References · 13

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2019End-to-End Open-Domain Question Answering with BERTserinicited by 152
  3. 2020Open-Domain Question Answering Goes Conversational via Question Rewritingcited by 148
  4. 2021SituatedQA: Incorporating Extra-Linguistic Contexts into QAcited by 145
  5. 2020Open-Retrieval Conversational Question Answeringcited by 99
  6. 2020Question Rewriting for Conversational Question Answeringcited by 93
  7. 2020Few-Shot Generative Conversational Query Rewritingcited by 85
  8. 2019BERT with History Answer Embedding for Conversational Question Answeringcited by 67
  9. 2020Query Resolution for Conversational Search with Limited Supervisioncited by 52
  10. 2019Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansioncited by 42
  11. 2019Attentive History Selection for Conversational Question Answeringcited by 33
  12. 2019Technical report on Conversational Question Answeringcited by 19
1 more
  1. 2021A Comparison of Question Rewriting Methods for Conversational Passage Retrievalcited by 16

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Cited by year

Citing papers per year: 2023: 2; 2024: 3.

5 papers.

  1. 2024Talk Less, Interact Better: Evaluating In-context Conversational Adaptation in Multimodal LLMscited by 18
  2. 2024Common Ground Tracking in Multimodal Dialoguecited by 13
  3. 2024Assessing "Implicit" Retrieval Robustness of Large Language Modelscited by 9
  4. 2023Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversationscited by 6
  5. 2023Strong and Efficient Baselines for Open Domain Conversational Question Answeringcited by 2