# QADiver: Interactive Framework for Diagnosing QA Models

arXiv: https://arxiv.org/abs/1812.00161 · HTML: https://mlsanity.com/paper/1812.00161/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2018.

## Built on by (most cited first, 3 of 3)

- [Label-Descriptive Patterns and Their Application to Characterizing Classification Errors](https://mlsanity.com/paper/2110.09599/) (2021)
- [MRCLens: an MRC Dataset Bias Detection Toolkit](https://mlsanity.com/paper/2207.08943/) (2022)
- [Explaining Math Word Problem Solvers](https://mlsanity.com/paper/2307.13128/) (2023)

## Abstract (arXiv)

Question answering (QA) extracting answers from text to the given question in natural language, has been actively studied and existing models have shown a promise of outperforming human performance when trained and evaluated with SQuAD dataset. However, such performance may not be replicated in the actual setting, for which we need to diagnose the cause, which is non-trivial due to the complexity of model. We thus propose a web-based UI that provides how each model contributes to QA performances, by integrating visualization and analysis tools for model explanation. We expect this framework can help QA model researchers to refine and improve their models.