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Code and Data for our EMNLP 2020 paper titled 'Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering'

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Code and Data for our EMNLP 2020 paper titled 'Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering'

Data

Data can be downloaded using instructions in data/ReadMe.md

Requirements

  • python 3.7.5
  • pytorch 1.6.0
  • allennlp 0.9.0
  • code/environment.yml is also provided

Code

Follow code/Readme.md

Reference

@inproceedings{jhamtani2020grc,
  title={Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering},
  author={Jhamtani, Harsh and Clark, Peter},
  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year={2020}
}

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Code and Data for our EMNLP 2020 paper titled 'Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering'

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