Language (re)modelling: Towards embodied language understanding

Ronen Tamari, Chen Shani, Tom Hope, Miriam R.L. Petruck, Omri Abend, Dafna Shahaf

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

Abstract

While natural language understanding (NLU) is advancing rapidly, today's technology differs from human-like language understanding in fundamental ways, notably in its inferior efficiency, interpretability, and generalization. This work proposes an approach to representation and learning based on the tenets of embodied cognitive linguistics (ECL). According to ECL, natural language is inherently executable (like programming languages), driven by mental simulation and metaphoric mappings over hierarchical compositions of structures and schemata learned through embodied interaction. This position paper argues that the use of grounding by metaphoric inference and simulation will greatly benefit NLU systems, and proposes a system architecture along with a roadmap towards realizing this vision.

Original languageEnglish
Title of host publicationACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages6268-6281
Number of pages14
ISBN (Electronic)9781952148255
StatePublished - 2020
Event58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States
Duration: 5 Jul 202010 Jul 2020

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics

Conference

Conference58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
Country/TerritoryUnited States
CityVirtual, Online
Period5/07/2010/07/20

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

Fingerprint

Dive into the research topics of 'Language (re)modelling: Towards embodied language understanding'. Together they form a unique fingerprint.

Cite this