12 citations · 22 across the 6 of their papers we have counts for
11 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…
Applying a Generic Sequence-to-Sequence Model for Simple and Effective Keyphrase Generation
Md Faisal Mahbub Chowdhury, Gaetano Rossiello, Michael Glass +2
In recent years, a number of keyphrase generation (KPG) approaches were proposed consisting of complex model architectures, dedicated training paradigms and decoding strategies. In…
Robust Retrieval Augmented Generation for Zero-shot Slot Filling
Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury +1
Automatically inducing high quality knowledge graphs from a given collection of documents still remains a challenging problem in AI. One way to make headway for this problem is thr…
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…