8 citations · 11 across the 5 of their papers we have counts for
9 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…
Topic Transferable Table Question Answering
Saneem Ahmed Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj +5
Weakly-supervised table question-answering(TableQA) models have achieved state-of-art performance by using pre-trained BERT transformer to jointly encoding a question and a table t…
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…
Type Prediction Systems
Sarthak Dash, Nandana Mihindukulasooriya, Alfio Gliozzo +1
Inferring semantic types for entity mentions within text documents is an important asset for many downstream NLP tasks, such as Semantic Role Labelling, Entity Disambiguation, Know…