title: Adversarial reading networks for machine comprehension
abstract: Machine reading has recently shown remarkable progress thanks to differentiable
  reasoning models. In this context, End-to-End trainable Memory Networks
  (MemN2N) have demonstrated promising performance on simple natural language
  based reasoning tasks such as factual reasoning and basic deduction. However,
  the task of machine comprehension is currently bounded to a supervised setting
  and available question answering dataset. In this paper we explore the paradigm
  of adversarial learning and self-play for the task of machine reading comprehension.
  Inspired by the successful propositions in the domain of game learning, we
  present a novel approach of training for this task that is based on the definition
  of a coupled attention-based memory model. On one hand, a reader network is
  in charge of finding answers regarding a passage of text and a question. On the
  other hand, a narrator network is in charge of obfuscating spans of text in order
  to minimize the probability of success of the reader. We experimented the model
  on several question-answering corpora. The proposed learning paradigm and associated
  models present encouraging results.
authors: Quentin Grail Julien Perez
hIndex: Quentin Grail 3