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Open Domain Question Answering over Tables via Dense Retrieval
[article]
2021
arXiv
pre-print
Recent advances in open-domain QA have led to strong models based on dense retrieval, but only focused on retrieving textual passages. In this work, we tackle open-domain QA over tables for the first time, and show that retrieval can be improved by a retriever designed to handle tabular context. We present an effective pre-training procedure for our retriever and improve retrieval quality with mined hard negatives. As relevant datasets are missing, we extract a subset of Natural Questions
arXiv:2103.12011v2
fatcat:jwrtnmv4lbhflf425xqmddyjsi