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Open Domain Question Answering over Tables via Dense Retrieval
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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.
Forward citations
Cited by 2 Pith papers
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WikiMixQA: A Multimodal Benchmark for Question Answering over Tables and Charts
WikiMixQA is a new 1,000-question benchmark for cross-modal table-and-chart reasoning, on which proprietary models drop from ~70% to ~55% accuracy when full Wikipedia pages are provided.
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