학술논문

Language (Re)modelling: Towards Embodied Language Understanding
Document Type
Working Paper
Source
Subject
Computer Science - Computation and Language
Computer Science - Machine Learning
Language
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.
Comment: Accepted to ACL2020 Theme Track. Extended bibliography version