8 citations · 14 across the 5 of their papers we have counts for
5 papers
End-to-End Table Question Answering via Retrieval-Augmented Generation
Feifei Pan, Mustafa Canim, Michael Glass +2
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…
Multi-Row, Multi-Span Distant Supervision For Table+Text Question
Vishwajeet Kumar, Yash Gupta, Saneem Chemmengath +4
Question answering (QA) over tables and linked text, also called TextTableQA, has witnessed significant research in recent years, as tables are often found embedded in documents al…
AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry
Yannis Katsis, Saneem Chemmengath, Vishwajeet Kumar +8
Recent advances in transformers have enabled Table Question Answering (Table QA) systems to achieve high accuracy and SOTA results on open domain datasets like WikiTableQuestions a…
CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answering
Feifei Pan, Mustafa Canim, Michael Glass +2
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 mos…
Capturing Row and Column Semantics in Transformer Based Question Answering over Tables
Michael Glass, Mustafa Canim, Alfio Gliozzo +7
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 technique…