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

• URL: http://arxiv.org/abs/2106.04441v2
• Date: Wed, 9 Jun 2021 17:09:53 GMT
• Title: CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answering
• Authors: Feifei Pan, Mustafa Canim, Michael Glass, Alfio Gliozzo, Peter Fox
• Abstract summary: We present the first end-to-end, transformer-based table question answering (QA) system. It 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. We introduce two new open-domain benchmarks, E2E_WTQ and E2E_GNQ, consisting of 2,005 natural language questions over 76,242 tables.
• Score: 8.389189333083513
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA [25.09488366689108]
  Text-to- parsing and end-to-end question answering (E2E TQA) are two main approaches for Table-based Question Answering task. Despite success on multiple benchmarks, they have yet to be compared and their synergy remains unexplored. We identify different strengths and weaknesses through evaluating state-of-the-art models on benchmark datasets.
  arXiv  Detail & Related papers  (2024-09-25T07:18:45Z)
• TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy [81.76462101465354]
  We present a novel large vision-hugging model, TabPedia, equipped with a concept synergy mechanism. This unified framework allows TabPedia to seamlessly integrate VTU tasks, such as table detection, table structure recognition, table querying, and table question answering. To better evaluate the VTU task in real-world scenarios, we establish a new and comprehensive table VQA benchmark, ComTQA.
  arXiv  Detail & Related papers  (2024-06-03T13:54:05Z)
• KET-QA: A Dataset for Knowledge Enhanced Table Question Answering [63.56707527868466]
  We propose to use a knowledge base (KB) as the external knowledge source for TableQA. Every question requires the integration of information from both the table and the sub-graph to be answered. We design a retriever-reasoner structured pipeline model to extract pertinent information from the vast knowledge sub-graph.
  arXiv  Detail & Related papers  (2024-05-13T18:26:32Z)
• Is Table Retrieval a Solved Problem? Exploring Join-Aware Multi-Table Retrieval [52.592071689901196]
  We introduce a method that uncovers useful join relations for any query and database during table retrieval. Our method outperforms the state-of-the-art approaches for table retrieval by up to 9.3% in F1 score and for end-to-end QA by up to 5.4% in accuracy.
  arXiv  Detail & Related papers  (2024-04-15T15:55:01Z)
• Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansion [57.53174887650989]
  Table question answering is a popular task that assesses a model's ability to understand and interact with structured data. Existing methods either convert both the table and external knowledge into text, which neglects the structured nature of the table. We propose a simple yet effective method to integrate external information in a given table.
  arXiv  Detail & Related papers  (2024-01-28T03:37:11Z)
• MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering [61.48881995121938]
  Real-world queries are complex in nature, often over multiple tables in a relational database or web page. Our model, MultiTabQA, not only answers questions over multiple tables, but also generalizes to generate tabular answers.
  arXiv  Detail & Related papers  (2023-05-22T08:25:15Z)
• A Survey on Table Question Answering: Recent Advances [10.874446530132087]
  Table Question Answering (Table QA) refers to providing precise answers from tables to answer a user's question. We classify existing methods for table QA into five categories according to their techniques. We identify and outline several key challenges and discuss the potential future directions of table QA.
  arXiv  Detail & Related papers  (2022-07-12T02:44:40Z)
• OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering [106.73213656603453]
  We develop a simple table-based QA model with minimal annotation effort. We propose an omnivorous pretraining approach that consumes both natural and synthetic data.
  arXiv  Detail & Related papers  (2022-07-08T01:23:45Z)
• End-to-End Table Question Answering via Retrieval-Augmented Generation [19.89730342792824]
  We introduce T-RAG, an end-to-end Table QA model, where a non-parametric dense vector index is fine-tuned jointly with BART, a parametric sequence-to-sequence model to generate answer tokens. Given any natural language question, T-RAG utilizes a unified pipeline to automatically search through a table corpus to directly locate the correct answer from the table cells.
  arXiv  Detail & Related papers  (2022-03-30T23:30:16Z)
• Multi-Row, Multi-Span Distant Supervision For Table+Text Question [33.809732338627136]
  Question answering (QA) over tables and linked text, also called TextTableQA, has witnessed significant research in recent years. We present MITQA, a transformer-based TextTableQA system that is explicitly designed to cope with distant supervision along both these axes.
  arXiv  Detail & Related papers  (2021-12-14T12:48:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.