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.
Benchmarks
These leaderboards are used to track progress in Table Retrieval
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Statcan Dialogue Dataset
Most implemented papers
Ad Hoc Table Retrieval using Semantic Similarity
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.
Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval
Tables contain valuable knowledge in a structured form.
Table Search Using a Deep Contextualized Language Model
Zhiyu-Chen/SIGIR2020-BERT-Table-Search
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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
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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
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
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
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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
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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
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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
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Optimizing accuracy and performance while eliminating hallucinations of open-domain conversational large language models (LLMs) is an open research challenge.
