Now showing items 1-3 of 3

    • Capturing Row and Column Semantics in Transformer Based Question Answering over Tables 

      Glass, Michael; Canim, Mustafa; Gliozzo, Alfio; Chemmengath, Saneem; Kumar, Vishwajeet; Chakravarti, Rishav; Sil, Avi; Pan, FeiFei; Bharadwaj, Samarth; Fauceglia, Nicolas Rodolfo (arXiv, 2021-04)
      Transformer based architectures are recently used for the task of answering questions over tables. In order to improve the accuracy on this task, specialized pre-training techniques have been developed and applied on ...
    • CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answering 

      Pan, FeiFei; Canim, Mustafa; Glass, M.; Gliozzo, A.; Fox, Peter (arXiv, 2021-06)
      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 ...
    • End-to-End Table Question Answering via Retrieval-Augmented Generation 

      Pan, FeiFei; Canim, Mustafa; Glass, Michael; Gliozzo, Alfio; Hendler, James A. (arXiv, 2022-03)
      Most existing end-to-end Table Question Answering (Table QA) models consist of a two-stage framework with a retriever to select relevant table candidates from a corpus and a reader to locate the correct answers from table ...