A Family of Cognitively Realistic Parsing Environments for Deep Reinforcement LearningDownload PDF

Oct 12, 2021 (edited Dec 02, 2021)Deep RL Workshop NeurIPS 2021Readers: Everyone
  • Keywords: RL environment, chart parsing, cognitive realism, Q-learning, Actor Critic
  • TL;DR: We introduce a family of cognitively realistic chart-parsing environments to evaluate potential psycholinguistic implications of RL algorithms.
  • Abstract: The hierarchical syntactic structure of natural language is a key feature of human cognition that enables us to recursively construct arbitrarily long sentences, supporting communication of complex, relational information. In this work, we describe a framework in which learning cognitively realistic left-corner parsers can be formalized as a Reinforcement Learning problem, and introduce a family of cognitively realistic chart-parsing environments to evaluate potential psycholinguistic implications of RL algorithms. We report how several baseline Q-learning and Actor Critic algorithms, both tabular and neural, perform on subsets of the Penn Treebank corpus. We observe a sharp increase in difficulty as parse trees get slightly more complex, indicating that hierarchical reinforcement learning might be required to solve this family of environments.
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