# Effective Representation to Capture Collaboration Behaviors between Explainer and User

arXiv: https://arxiv.org/abs/2201.03147 · HTML: https://mlsanity.com/paper/2201.03147/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2022.

## Built on by (most cited first, 4 of 4)

- [Attention cannot be an Explanation](https://mlsanity.com/paper/2201.11194/) (2022)
- [Question Generation for Evaluating Cross-Dataset Shifts in Multi-modal Grounding](https://mlsanity.com/paper/2201.09639/) (2022)
- [Discourse Analysis for Evaluating Coherence in Video Paragraph Captions](https://mlsanity.com/paper/2201.06207/) (2022)
- [On the Importance of Karaka Framework in Multi-modal Grounding](https://mlsanity.com/paper/2204.04347/) (2022)

## Abstract (arXiv)

An explainable AI (XAI) model aims to provide transparency (in the form of justification, explanation, etc) for its predictions or actions made by it. Recently, there has been a lot of focus on building XAI models, especially to provide explanations for understanding and interpreting the predictions made by deep learning models. At UCLA, we propose a generic framework to interact with an XAI model in natural language.