# Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations

arXiv: https://arxiv.org/abs/2311.01273 · HTML: https://mlsanity.com/paper/2311.01273/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks](https://mlsanity.com/paper/2302.08399/) (2023)
- [Neural models of factuality](https://mlsanity.com/paper/1804.02472/) (2018)
- [MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logic](https://mlsanity.com/paper/2305.03353/) (2023)
- [Lexicosyntactic Inference in Neural Models](https://mlsanity.com/paper/1808.06232/) (2018)
- [He Thinks He Knows Better than the Doctors: BERT for Event Factuality Fails on Pragmatics](https://mlsanity.com/paper/2107.00807/) (2021)
- [Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures](https://mlsanity.com/paper/1907.03227/) (2019)
- [From Rewriting to Remembering: Common Ground for Conversational QA Models](https://mlsanity.com/paper/2204.03930/) (2022)

## Built on by (most cited first, 6 of 6)

- [Views Are My Own, but Also Yours: Benchmarking Theory of Mind Using Common Ground](https://mlsanity.com/paper/2403.02451/) (2024)
- [Understanding Common Ground Misalignment in Goal-Oriented Dialog: A Case-Study with Ubuntu Chat Logs](https://mlsanity.com/paper/2503.12370/) (2025)
- [It Couldn't Help But Overhear: On the Limits of Modelling Meta-Communicative Grounding Acts with Supervised Learning](https://mlsanity.com/paper/2405.01139/) (2024)
- [Multimodal Belief Prediction](https://mlsanity.com/paper/2406.07466/) (2024)
- [Synthetic Audio Helps for Cognitive State Tasks](https://mlsanity.com/paper/2502.06922/) (2025)
- [Illusion of Alignment: Detecting Hidden Disagreement in Collaborative Dialogue](https://mlsanity.com/paper/2608.08210/) (2026)

## Abstract (arXiv)

When we communicate with other humans, we do not simply generate a sequence of words. Rather, we use our cognitive state (beliefs, desires, intentions) and our model of the audience's cognitive state to create utterances that affect the audience's cognitive state in the intended manner. An important part of cognitive state is the common ground, which is the content the speaker believes, and the speaker believes the audience believes, and so on. While much attention has been paid to common ground in cognitive science, there has not been much work in natural language processing. In this paper, we introduce a new annotation and corpus to capture common ground. We then describe some initial experiments extracting propositions from dialog and tracking their status in the common ground from the perspective of each speaker.