# Modeling Non-Cooperative Dialogue: Theoretical and Empirical Insights

arXiv: https://arxiv.org/abs/2207.07255 · HTML: https://mlsanity.com/paper/2207.07255/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 69 references that are in the map.
- First posted on arXiv 15 Jul 2022.

## Builds on (most cited first, 23 of 23)

- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data](https://mlsanity.com/paper/1703.11008/) (2017)
- [Visual Dialog](https://mlsanity.com/paper/1611.08669/) (2016)
- [Detecting and Correcting for Label Shift with Black Box Predictors](https://mlsanity.com/paper/1802.03916/) (2018)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift](https://mlsanity.com/paper/1810.11953/) (2018)
- [GuessWhat?! Visual object discovery through multi-modal dialogue](https://mlsanity.com/paper/1611.08481/) (2016)
- [Support and Invertibility in Domain-Invariant Representations](https://mlsanity.com/paper/1903.03448/) (2019)
- [Adversarial examples from computational constraints](https://mlsanity.com/paper/1805.10204/) (2018)
- [Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment](https://mlsanity.com/paper/1903.01689/) (2019)
- [The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue](https://mlsanity.com/paper/1906.01530/) (2019)
- [Reducing Adversarially Robust Learning to Non-Robust PAC Learning](https://mlsanity.com/paper/2010.12039/) (2020)
- [Deception Detection in Videos](https://mlsanity.com/paper/1712.04415/) (2017)
- [Multi-Domain Adversarial Learning](https://mlsanity.com/paper/1903.09239/) (2019)
- [End-to-end optimization of goal-driven and visually grounded dialogue systems](https://mlsanity.com/paper/1703.05423/) (2017)
- [PAC-Bayes and Domain Adaptation](https://mlsanity.com/paper/1707.05712/) (2017)
- [Resolving Intent Ambiguities by Retrieving Discriminative Clarifying Questions](https://mlsanity.com/paper/2008.07559/) (2020)
- [PAC-Bayesian Domain Adaptation Bounds for Multiclass Learners](https://mlsanity.com/paper/2207.05685/) (2022)
- [Lower Bounds for Adversarially Robust PAC Learning](https://mlsanity.com/paper/1906.05815/) (2019)
- [The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error](https://mlsanity.com/paper/2203.11317/) (2022)
- [Adversarial Examples - A Complete Characterisation of the Phenomenon](https://mlsanity.com/paper/1810.01185/) (2018)
- [Grounded Agreement Games: Emphasizing Conversational Grounding in Visual Dialogue Settings](https://mlsanity.com/paper/1908.11279/) (2019)
- [PAC Bayesian Performance Guarantees for Deep (Stochastic) Networks in Medical Imaging](https://mlsanity.com/paper/2104.05600/) (2021)

## Built on by (most cited first, 2 of 2)

- [LEATHER: A Framework for Learning to Generate Human-like Text in Dialogue](https://mlsanity.com/paper/2210.07777/) (2022)
- [Pointing out Human Answer Mistakes in a Goal-Oriented Visual Dialogue](https://mlsanity.com/paper/2309.10375/) (2023)

## Abstract (arXiv)

Investigating cooperativity of interlocutors is central in studying pragmatics of dialogue. Models of conversation that only assume cooperative agents fail to explain the dynamics of strategic conversations. Thus, we investigate the ability of agents to identify non-cooperative interlocutors while completing a concurrent visual-dialogue task. Within this novel setting, we study the optimality of communication strategies for achieving this multi-task objective. We use the tools of learning theory to develop a theoretical model for identifying non-cooperative interlocutors and apply this theory to analyze different communication strategies. We also introduce a corpus of non-cooperative conversations about images in the GuessWhat?! dataset proposed by De Vries et al. (2017). We use reinforcement learning to implement multiple communication strategies in this context and find empirical results validate our theory.