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An original implementation of EMNLP 2020, "AmbigQA: Answering Ambiguous Open-domain Questions"

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CAmbigQA

Codes inside this repository is largely based on codes ("https://github.com/shmsw25/AmbigQA/tree/main/") for the paper AmbigQA: Answering Ambiguous Open-domain Questions (EMNLP 2020) by Sewon Min, Julian Michael, Hannaneh Hajishirzi, and Luke Zettlemoyer.

Check the codes folder for the baseline model training and inference.

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An original implementation of EMNLP 2020, "AmbigQA: Answering Ambiguous Open-domain Questions"

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  • Python 59.8%
  • Jupyter Notebook 37.1%
  • Shell 3.1%