shmsw25/AmbigQA

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

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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.

Natural Language Processing Question Answering Machine Learning Research Data Annotation Information Retrieval
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 8 / 25
Community 20 / 25

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121

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23

Language

Python

License

Last pushed

Apr 23, 2022

Commits (30d)

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