CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answering

TL;DR

CLTR is a novel transformer-based system that performs end-to-end table retrieval and question answering, effectively handling complex tables and providing visual cues for answer localization.

Contribution

This paper introduces CLTR, the first end-to-end transformer-based architecture for table retrieval and question answering, along with new benchmarks for open-domain table QA.

Findings

CLTR achieves state-of-the-art performance in table retrieval.

The system effectively locates answer cells in complex tables.

New benchmarks E2E_WTQ and E2E_GNQ support future research.

Abstract

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. Our system, CLTR, extends the current state-of-the-art QA over tables model to build an end-to-end table QA architecture. This system has successfully tackled many real-world table QA problems with a simple, unified pipeline. Our proposed system can also generate a heatmap of candidate columns and rows over complex tables and allow users to quickly identify the correct cells to answer questions. In addition, we introduce two new open-domain benchmarks, E2E_WTQ and E2E_GNQ, consisting of 2,005 natural language questions over 76,242 tables. The benchmarks are designed to validate CLTR as well as accommodate future table retrieval and end-to-end table QA research and experiments. Our experiments demonstrate that our system is the current state-of-the-art model on the table retrieval task and produces promising results for end-to-end table QA.