Table Retrieval

13 papers with code • 1 benchmarks • 1 datasets

When given a query, the goal of this task is to retrieve a relevant table from a (potentially large) collection of tables. The query could be a single sentence (such as a question), or it could also be a conversation. As for the retrieval, the tables could be in the raw form (i.e. the values of each cells), the metadata (such as the title, description), or summary statistics.

Most implemented papers

Ad Hoc Table Retrieval using Semantic Similarity

iai-group/www2018-table 16 Feb 2018

Specifically, we (i) represent queries and tables in multiple semantic spaces (both discrete sparse and continuous dense vector representations) and (ii) introduce various similarity measures for matching those semantic representations.

Table Search Using a Deep Contextualized Language Model

Zhiyu-Chen/SIGIR2020-BERT-Table-Search 19 May 2020

Pretrained contextualized language models such as BERT have achieved impressive results on various natural language processing benchmarks.

Retrieving Complex Tables with Multi-Granular Graph Representation Learning

FeiWang96/GTR 4 May 2021

The task of natural language table retrieval (NLTR) seeks to retrieve semantically relevant tables based on natural language queries.

WTR: A Test Collection for Web Table Retrieval

Zhiyu-Chen/Web-Table-Retrieval-Benchmark 5 May 2021

We describe the development, characteristics and availability of a test collection for the task of Web table retrieval, which uses a large-scale Web Table Corpora extracted from the Common Crawl.

Semantic Table Retrieval using Keyword and Table Queries

iai-group/table-retrieval 13 May 2021

The main novel contribution of this work is a semantic table retrieval framework for matching information needs (keyword or table queries) against tables.

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

IBM/row-column-intersection 8 Jun 2021

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.

StruBERT: Structure-aware BERT for Table Search and Matching

medtray/StruBERT 27 Mar 2022

In this paper, we propose StruBERT, a structure-aware BERT model that fuses the textual and structural information of a data table to produce context-aware representations for both textual and tabular content of a data table.

Table Retrieval May Not Necessitate Table-specific Model Design

zorazrw/nqt-retrieval NAACL (SUKI) 2022

In this work, we focus on the task of table retrieval, and ask: "is table-specific model design necessary for table retrieval, or can a simpler text-based model be effectively used to achieve a similar result?"

cTBLS: Augmenting Large Language Models with Conversational Tables

avalab-gt/ctbls 21 Mar 2023

Optimizing accuracy and performance while eliminating hallucinations of open-domain conversational large language models (LLMs) is an open research challenge.