Explanatory and actionable debugging for machine learning: A tableQA demonstration

Minseok Cho, Gyeongbok Lee, Seung Won Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Question answering from tables (TableQA) extracting answers from tables from the question given in natural language, has been actively studied. Existing models have been trained and evaluated mostly with respect to answer accuracy using public benchmark datasets such as WikiSQL. The goal of this demonstration is to show a debugging tool for such models, explaining answers to humans, known as explanatory debugging. Our key distinction is making it “actionable" to allow users to directly correct models upon explanation. Specifically, our tool surfaces annotation and models errors for users to correct, and provides actionable insights.

Original languageEnglish
Title of host publicationSIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages1333-1336
Number of pages4
ISBN (Electronic)9781450361729
DOIs
Publication statusPublished - 2019 Jul 18
Event42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019 - Paris, France
Duration: 2019 Jul 212019 Jul 25

Publication series

NameSIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019
CountryFrance
CityParis
Period19/7/2119/7/25

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Applied Mathematics
  • Software

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  • Cite this

    Cho, M., Lee, G., & Hwang, S. W. (2019). Explanatory and actionable debugging for machine learning: A tableQA demonstration. In SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1333-1336). (SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval). Association for Computing Machinery, Inc. https://doi.org/10.1145/3331184.3331404