NLPGym -- A toolkit for evaluating RL agents on Natural Language Processing Tasks

November 16, 2020 · Entered Twilight · 🏛 arXiv.org

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Repo contents: .circleci, .github, .gitignore, LICENSE, README.md, assets, demo_scripts, nlp_gym, setup.py, tests

Authors Rajkumar Ramamurthy, Rafet Sifa, Christian Bauckhage arXiv ID 2011.08272 Category cs.CL: Computation & Language Cross-listed cs.AI Citations 5 Venue arXiv.org Repository https://github.com/rajcscw/nlp-gym ⭐ 202 Last Checked 2 months ago

Abstract

Reinforcement learning (RL) has recently shown impressive performance in complex game AI and robotics tasks. To a large extent, this is thanks to the availability of simulated environments such as OpenAI Gym, Atari Learning Environment, or Malmo which allow agents to learn complex tasks through interaction with virtual environments. While RL is also increasingly applied to natural language processing (NLP), there are no simulated textual environments available for researchers to apply and consistently benchmark RL on NLP tasks. With the work reported here, we therefore release NLPGym, an open-source Python toolkit that provides interactive textual environments for standard NLP tasks such as sequence tagging, multi-label classification, and question answering. We also present experimental results for 6 tasks using different RL algorithms which serve as baselines for further research. The toolkit is published at https://github.com/rajcscw/nlp-gym