@InProceedings{burns-EtAl:2018:BlackboxNLP,
  author    = {Burns, Kaylee  and  Nematzadeh, Aida  and  Grant, Erin  and  Gopnik, Alison  and  Griffiths, Tom},
  title     = {Exploiting Attention to Reveal Shortcomings in Memory Models},
  booktitle = {Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP},
  month     = {November},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {378--380},
  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.},
  url       = {http://www.aclweb.org/anthology/W18-5454}
}