Language (Re)modelling: Towards Embodied Language Understanding

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


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.
Anthology ID:
2020.acl-main.559
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6268–6281
Language:
URL:
https://aclanthology.org/2020.acl-main.559
DOI:
10.18653/v1/2020.acl-main.559
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.559.pdf
Video:
 http://slideslive.com/38929397