QADiver: Interactive Framework for Diagnosing QA Models

Authors

  • Gyeongbok Lee Yonsei University
  • Sungdong Kim NAVER Corporation
  • Seung-won Hwang Yonsei University

DOI:

https://doi.org/10.1609/aaai.v33i01.33019861

Abstract

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.

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Published

2019-07-17

How to Cite

Lee, G., Kim, S., & Hwang, S.- won. (2019). QADiver: Interactive Framework for Diagnosing QA Models. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9861-9862. https://doi.org/10.1609/aaai.v33i01.33019861

Issue

Section

Demonstration Track