shmsw25/AmbigQA
An original implementation of EMNLP 2020, "AmbigQA: Answering Ambiguous Open-domain Questions"
This project helps researchers in natural language processing (NLP) develop and evaluate systems that can answer ambiguous open-domain questions. It provides a dataset, AmbigNQ, which includes questions that have multiple valid interpretations and corresponding multiple answers or rephrased question-answer pairs. NLP researchers and machine learning engineers can use this to train and benchmark models for complex question answering.
121 stars. No commits in the last 6 months.
Use this if you are an NLP researcher working on question answering systems and need a dataset to train and evaluate models on questions that might have more than one correct answer depending on interpretation.
Not ideal if you are looking for an out-of-the-box question answering system for direct use in an application or if your questions are always unambiguous with single, definitive answers.
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Apr 23, 2022
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