Exploiting Attention to Reveal Shortcomings in Memory Models

Kaylee Burns, Aida Nematzadeh, Erin Grant, Alison Gopnik, Thomas L. Griffiths

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

2 Scopus citations

Abstract

The decision making processes of deep networks are difficult to understand and while their accuracy often improves with increased architectural complexity, so too does their opacity. Practical use of machine learning models, especially for question and answering applications, demands a system that is interpretable. We analyze the attention of a memory network model to reconcile contradictory performance on a challenging question-answering dataset that is inspired by theory-of-mind experiments. We equate success on questions to task classification, which explains not only test-time failures but also how well the model generalizes to new training conditions.

Original languageEnglish (US)
Title of host publicationEMNLP 2018 - 2018 EMNLP Workshop BlackboxNLP
Subtitle of host publicationAnalyzing and Interpreting Neural Networks for NLP, Proceedings of the 1st Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages378-380
Number of pages3
ISBN (Electronic)9781948087711
StatePublished - 2018
Event1st Workshop on BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, co-located with the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 - Brussels, Belgium
Duration: Nov 1 2018 → …

Publication series

NameEMNLP 2018 - 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, Proceedings of the 1st Workshop

Conference

Conference1st Workshop on BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, co-located with the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
Country/TerritoryBelgium
CityBrussels
Period11/1/18 → …

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computational Theory and Mathematics
  • Information Systems

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