bernhard2202/rankqa
This is the PyTorch implementation of the ACL 2019 paper RankQA: Neural Question Answering with Answer Re-Ranking.
This project helps researchers and developers working with question answering systems to improve the accuracy of their results. It takes existing question-text pairs and their initial predicted answers, then applies an additional re-ranking step using a combination of features. The output is a refined list of answers, ordered by their likelihood. This is primarily for natural language processing (NLP) researchers and engineers building or evaluating advanced QA systems.
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Use this if you are building a neural question-answering system and want to boost the accuracy of the extracted answers beyond what traditional two-stage retrieval and comprehension models offer.
Not ideal if you are looking for an out-of-the-box, end-user question-answering tool without needing to integrate it into an existing NLP pipeline.
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Dec 13, 2021
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