Web Table Retrieval using Multimodal Deep Learning

Roee Shraga, Haggai Roitman, Guy Feigenblat, Mustafa Cannim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

30 Scopus citations

Abstract

We address the web table retrieval task, aiming to retrieve and rank web tables as whole answers to a given information need. To this end, we formally define web tables as multimodal objects. We then suggest a neural ranking model, termed MTR, which makes a novel use of Gated Multimodal Units (GMUs) to learn a joint-representation of the query and the different table modalities. We further enhance this model with a co-learning approach which utilizes automatically learned query-independent and query-dependent "helper" labels. We evaluate the proposed solution using both ad hoc queries (WikiTables) and natural language questions (GNQtables). Overall, we demonstrate that our approach surpasses the performance of previously studied state-of-the-art baselines.

Original languageEnglish
Title of host publicationSIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages1399-1408
Number of pages10
ISBN (Electronic)9781450380164
DOIs
StatePublished - 25 Jul 2020
Externally publishedYes
Event43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020 - Virtual, Online, China
Duration: 25 Jul 202030 Jul 2020

Publication series

NameSIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020
Country/TerritoryChina
CityVirtual, Online
Period25/07/2030/07/20

Bibliographical note

Publisher Copyright:
© 2020 ACM.

Keywords

  • experimentation
  • multimodal deep-learning
  • table retrieval

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