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Found 6 papers, 3 papers with code
End-to-End Table Question Answering via Retrieval-Augmented Generation
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 candidates.
Multi-Row, Multi-Span Distant Supervision For Table+Text Question
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 along with related text.
CLTR: An End-to-End, Transformer-Based System for Cell-Level Table Retrieval and Table Question Answering
We present the first end-to-end, transformer-based table question answering (QA) system that takes natural language questions and massive table corpora as inputs to retrieve the most relevant tables and locate the correct table cells to answer the question.
AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry
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 and WikiSQL.
CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answering
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.
Capturing Row and Column Semantics in Transformer Based Question Answering over Tables
While this model yields extremely high accuracy at finding cell values on recent benchmarks, a second model we propose, called RCI representation, provides a significant efficiency advantage for online QA systems over tables by materializing embeddings for existing tables.
