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We present the first end-to-end, transformer-based table question answering (QA) system that takes natural language questions and massive table corpus as inputs to retrieve the most relevant tables and locate the correct table cells to answer the question.
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Semtab 2019: Resources to benchmark tabular data to knowledge graph matching systems
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Natural questions: a benchmark for question answering research
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Web table retrieval using multimodal deep learning
Roee Shraga, Haggai Roitman, Guy Feigenblat, and Mustafa Cannim. 2020c
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Ad hoc table retrieval using intrinsic and extrinsic similarities
Roee Shraga, Haggai Roitman, Guy Feigenblat, and Mustafa Canim. 2020a · 2020
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Ad hoc table retrieval using intrinsic and extrinsic similarities
Roee Shraga, Haggai Roitman, Guy Feigenblat, and Mustafa Canim. 2020b · 2020
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TaBERT: Pretraining for joint understanding of textual and tabular data
Pengcheng Yin, Graham Neubig, Wen-tau Yih, and Sebastian Riedel. 2020 · 2020
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Open question answering over tables and text
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