@inproceedings{tamari-etal-2020-language,
title = "{L}anguage (Re)modelling: {T}owards Embodied Language Understanding",
author = "Tamari, Ronen and
Shani, Chen and
Hope, Tom and
Petruck, Miriam R L and
Abend, Omri and
Shahaf, Dafna",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.559",
doi = "10.18653/v1/2020.acl-main.559",
pages = "6268--6281",
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 reasoning and simulation will greatly benefit NLU systems, and proposes a system architecture along with a roadmap towards realizing this vision.",
}
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<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 reasoning and simulation will greatly benefit NLU systems, and proposes a system architecture along with a roadmap towards realizing this vision.</abstract>
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%0 Conference Proceedings
%T Language (Re)modelling: Towards Embodied Language Understanding
%A Tamari, Ronen
%A Shani, Chen
%A Hope, Tom
%A Petruck, Miriam R. L.
%A Abend, Omri
%A Shahaf, Dafna
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F tamari-etal-2020-language
%X 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 reasoning and simulation will greatly benefit NLU systems, and proposes a system architecture along with a roadmap towards realizing this vision.
%R 10.18653/v1/2020.acl-main.559
%U https://aclanthology.org/2020.acl-main.559
%U https://doi.org/10.18653/v1/2020.acl-main.559
%P 6268-6281
Markdown (Informal)
[Language (Re)modelling: Towards Embodied Language Understanding](https://aclanthology.org/2020.acl-main.559) (Tamari et al., ACL 2020)
ACL
- Ronen Tamari, Chen Shani, Tom Hope, Miriam R L Petruck, Omri Abend, and Dafna Shahaf. 2020. Language (Re)modelling: Towards Embodied Language Understanding. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6268–6281, Online. Association for Computational Linguistics.