text: 365,190,1374366178966142980,1056626853652426752,Jonathan Herzig,"1/4 Much focus is given to dense retrieval of textual passages, but how should we design retrievers for tables in the context of open domain QA? New #NAACL2021 short paper: <LINK> With @muelletm, Syrine Krichene and @eisenjulian 2/4 We tackle open-domain QA over tables, and show that a BERT based retriever can be improved by a retriever designed to handle tabular context based on TAPAS.  We present an effective pre-training procedure for our retriever and improve its quality with mined hard negatives. 3/4 We extract an 11K subset of Natural Questions where answers reside in a table into a Table QA dataset (called NQ-tables), and find that our retriever improves both retrieval results and end-to-end QA results over BERT and BM25 baselines. 4/4 Our code for generating the NQ-tables dataset, and model checkpoints will be available soon in our repository: <LINK> And here is a figure as well 😀: <LINK>",https://arxiv.org/abs/2103.12011,"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 (Kwiatkowski et al., 2019) into a Table QA dataset. We find that our retriever improves retrieval results from 72.0 to 81.1 recall@10 and end-to-end QA results from 33.8 to 37.7 exact match, over a BERT based retriever. ",Open Domain Question Answering over Tables via Dense Retrieval,5,"['1/4 Much focus is given to dense retrieval of textual passages, but how should we design retrievers for tables in the context of open domain QA?\n\nNew #NAACL2021 short paper: <LINK>\n\nWith @muelletm, Syrine Krichene and @eisenjulian', '2/4 We tackle open-domain QA over tables, and show that a BERT based retriever can be improved by a retriever designed to handle tabular context based on TAPAS. \n\nWe present an effective pre-training procedure for our retriever and improve its quality with mined hard negatives.', '3/4 We extract an 11K subset of Natural Questions where answers reside in a table into a Table QA dataset (called NQ-tables), and find that our retriever improves both retrieval results and end-to-end QA results over BERT and BM25 baselines.', '4/4 Our code for generating the NQ-tables dataset, and model checkpoints will be available soon in our repository:\n\nhttps://t.co/Nh6mWkaUiL', 'And here is a figure as well 😀: https://t.co/5MB8kpEiy3']",21,03,909