# From Rewriting to Remembering: Common Ground for Conversational QA Models

arXiv: https://arxiv.org/abs/2204.03930 · HTML: https://mlsanity.com/paper/2204.03930/

## 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 (most cited first, 13 of 13)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [End-to-End Open-Domain Question Answering with BERTserini](https://mlsanity.com/paper/1902.01718/) (2019)
- [Open-Domain Question Answering Goes Conversational via Question Rewriting](https://mlsanity.com/paper/2010.04898/) (2020)
- [SituatedQA: Incorporating Extra-Linguistic Contexts into QA](https://mlsanity.com/paper/2109.06157/) (2021)
- [Open-Retrieval Conversational Question Answering](https://mlsanity.com/paper/2005.11364/) (2020)
- [Question Rewriting for Conversational Question Answering](https://mlsanity.com/paper/2004.14652/) (2020)
- [Few-Shot Generative Conversational Query Rewriting](https://mlsanity.com/paper/2006.05009/) (2020)
- [BERT with History Answer Embedding for Conversational Question Answering](https://mlsanity.com/paper/1905.05412/) (2019)
- [Query Resolution for Conversational Search with Limited Supervision](https://mlsanity.com/paper/2005.11723/) (2020)
- [Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion](https://mlsanity.com/paper/1910.03262/) (2019)
- [Attentive History Selection for Conversational Question Answering](https://mlsanity.com/paper/1908.09456/) (2019)
- [Technical report on Conversational Question Answering](https://mlsanity.com/paper/1909.10772/) (2019)
- [A Comparison of Question Rewriting Methods for Conversational Passage Retrieval](https://mlsanity.com/paper/2101.07382/) (2021)

## Built on by (most cited first, 5 of 5)

- [Talk Less, Interact Better: Evaluating In-context Conversational Adaptation in Multimodal LLMs](https://mlsanity.com/paper/2408.01417/) (2024)
- [Common Ground Tracking in Multimodal Dialogue](https://mlsanity.com/paper/2403.17284/) (2024)
- [Assessing "Implicit" Retrieval Robustness of Large Language Models](https://mlsanity.com/paper/2406.18134/) (2024)
- [Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations](https://mlsanity.com/paper/2311.01273/) (2023)
- [Strong and Efficient Baselines for Open Domain Conversational Question Answering](https://mlsanity.com/paper/2310.14708/) (2023)

## 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.