Language (re)modelling: Towards embodied language understanding

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

doi:10.18653/v1/2020.acl-main.559

Language (re)modelling: Towards embodied language understanding

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Scopus citations

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 language	American English
Title of host publication	ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Editors	Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Publisher	Association for Computational Linguistics (ACL)
Pages	6268-6281
Number of pages	14
ISBN (Electronic)	9781952148255
DOIs	https://doi.org/10.18653/v1/2020.acl-main.559
State	Published - 2020
Event	58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States Duration: 5 Jul 2020 → 10 Jul 2020

Publication series

Name	Proceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)	0736-587X

Conference

Conference	58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
Country/Territory	United States
City	Virtual, Online
Period	5/07/20 → 10/07/20

Bibliographical note

Funding Information:
We thank the reviewers for their insightful comments. This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation pro-gramme (grant no. 852686, SIAM) and NSF-BSF grant no. 2017741 (Shahaf), as well as the Israel Science Foundation grant no. 929/17 (Abend).

Funding Information:
We thank the reviewers for their insightful comments. This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant no. 852686, SIAM) and NSF-BSF grant no. 2017741 (Shahaf), as well as the Israel Science Foundation grant no. 929/17 (Abend).

Publisher Copyright:
© 2020 Association for Computational Linguistics

Keywords

natural language understanding
NLU
embodied cognitive linguistics
ECL
Computational Linguistics

Access to Document

10.18653/v1/2020.acl-main.559License: Unspecified

Cite this

Tamari, R., Shani, C., Hope, T., Petruck, M. R. L., Abend, O., & Shahaf, D. (2020). Language (re)modelling: Towards embodied language understanding. In D. Jurafsky, J. Chai, N. Schluter, & J. Tetreault (Eds.), ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (pp. 6268-6281). (Proceedings of the Annual Meeting of the Association for Computational Linguistics). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.acl-main.559

Tamari, Ronen ; Shani, Chen ; Hope, Tom et al. / Language (re)modelling : Towards embodied language understanding. ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. editor / Dan Jurafsky ; Joyce Chai ; Natalie Schluter ; Joel Tetreault. Association for Computational Linguistics (ACL), 2020. pp. 6268-6281 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

@inproceedings{173dc42456754aab8406f56837f1451e,

title = "Language (re)modelling: Towards embodied language understanding",

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.",

keywords = "natural language understanding, NLU, embodied cognitive linguistics, ECL, Computational Linguistics",

author = "Ronen Tamari and Chen Shani and Tom Hope and Petruck, {Miriam R.L.} and Omri Abend and Dafna Shahaf",

note = "Funding Information: We thank the reviewers for their insightful comments. This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation pro-gramme (grant no. 852686, SIAM) and NSF-BSF grant no. 2017741 (Shahaf), as well as the Israel Science Foundation grant no. 929/17 (Abend). Funding Information: We thank the reviewers for their insightful comments. This work was supported by the European Research Council (ERC) under the European Union{\textquoteright}s Horizon 2020 research and innovation programme (grant no. 852686, SIAM) and NSF-BSF grant no. 2017741 (Shahaf), as well as the Israel Science Foundation grant no. 929/17 (Abend). Publisher Copyright: {\textcopyright} 2020 Association for Computational Linguistics; 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 ; Conference date: 05-07-2020 Through 10-07-2020",

year = "2020",

doi = "10.18653/v1/2020.acl-main.559",

language = "American English",

series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",

publisher = "Association for Computational Linguistics (ACL)",

pages = "6268--6281",

editor = "Dan Jurafsky and Joyce Chai and Natalie Schluter and Joel Tetreault",

booktitle = "ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference",

address = "United States",

}

Tamari, R, Shani, C, Hope, T, Petruck, MRL, Abend, O & Shahaf, D 2020, Language (re)modelling: Towards embodied language understanding. in D Jurafsky, J Chai, N Schluter & J Tetreault (eds), ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. Proceedings of the Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics (ACL), pp. 6268-6281, 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Virtual, Online, United States, 5/07/20. https://doi.org/10.18653/v1/2020.acl-main.559

Language (re)modelling: Towards embodied language understanding. / Tamari, Ronen; Shani, Chen; Hope, Tom et al.
ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. ed. / Dan Jurafsky; Joyce Chai; Natalie Schluter; Joel Tetreault. Association for Computational Linguistics (ACL), 2020. p. 6268-6281 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Language (re)modelling

T2 - 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

AU - Tamari, Ronen

AU - Shani, Chen

AU - Hope, Tom

AU - Petruck, Miriam R.L.

AU - Abend, Omri

AU - Shahaf, Dafna

N1 - Funding Information: We thank the reviewers for their insightful comments. This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation pro-gramme (grant no. 852686, SIAM) and NSF-BSF grant no. 2017741 (Shahaf), as well as the Israel Science Foundation grant no. 929/17 (Abend). Funding Information: We thank the reviewers for their insightful comments. This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant no. 852686, SIAM) and NSF-BSF grant no. 2017741 (Shahaf), as well as the Israel Science Foundation grant no. 929/17 (Abend). Publisher Copyright: © 2020 Association for Computational Linguistics

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - natural language understanding

KW - NLU

KW - embodied cognitive linguistics

KW - ECL

KW - Computational Linguistics

UR - http://www.scopus.com/inward/record.url?scp=85107862690&partnerID=8YFLogxK

U2 - 10.18653/v1/2020.acl-main.559

DO - 10.18653/v1/2020.acl-main.559

M3 - Conference contribution

T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics

SP - 6268

EP - 6281

BT - ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

A2 - Jurafsky, Dan

A2 - Chai, Joyce

A2 - Schluter, Natalie

A2 - Tetreault, Joel

PB - Association for Computational Linguistics (ACL)

Y2 - 5 July 2020 through 10 July 2020

ER -

Tamari R, Shani C, Hope T, Petruck MRL, Abend O , Shahaf D. Language (re)modelling: Towards embodied language understanding. In Jurafsky D, Chai J, Schluter N, Tetreault J, editors, ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL). 2020. p. 6268-6281. (Proceedings of the Annual Meeting of the Association for Computational Linguistics). doi: 10.18653/v1/2020.acl-main.559

Language (re)modelling: Towards embodied language understanding

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this