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Talking Space: inference from spatial linguistic meanings (2109.06554v2)

Published 14 Sep 2021 in cs.CL, cs.AI, and cs.LO

Abstract: This paper concerns the intersection of natural language and the physical space around us in which we live, that we observe and/or imagine things within. Many important features of language have spatial connotations, for example, many prepositions (like in, next to, after, on, etc.) are fundamentally spatial. Space is also a key factor of the meanings of many words/phrases/sentences/text, and space is a, if not the key, context for referencing (e.g. pointing) and embodiment. We propose a mechanism for how space and linguistic structure can be made to interact in a matching compositional fashion. Examples include Cartesian space, subway stations, chesspieces on a chess-board, and Penrose's staircase. The starting point for our construction is the DisCoCat model of compositional natural language meaning, which we relax to accommodate physical space. We address the issue of having multiple agents/objects in a space, including the case that each agent has different capabilities with respect to that space, e.g., the specific moves each chesspiece can make, or the different velocities one may be able to reach. Once our model is in place, we show how inferences drawing from the structure of physical space can be made. We also how how linguistic model of space can interact with other such models related to our senses and/or embodiment, such as the conceptual spaces of colour, taste and smell, resulting in a rich compositional model of meaning that is close to human experience and embodiment in the world.

Citations (5)

Summary

  • The paper introduces an innovative compositional framework that integrates spatial relations into language models.
  • It extends the DisCoCat model to represent diverse spatial scenarios, from Cartesian grids to chessboard movements.
  • The integration of linguistic and spatial reasoning in the model offers promising avenues for advanced AI and cognitive research.

Integrating Spatial Concepts within Compositional LLMs

Overview of the Research

Vincent Wang-Macianica and Bob Coecke's work presents an innovative approach to understanding how spatial concepts integrate with language, a core component of human cognition and communication. They propose a model that allows the inference of spatial relations and the interaction between spatially descriptive language and the physical world, using examples that range from simple Cartesian coordinates to the intricacies of a chessboard.

Key Concepts and Model Foundation

The authors build upon the DisCoCat (Distributional Compositional Categorical) model of compositional natural language meaning, extending it to incorporate spatial relations. This involves creating a system where spatial relations between objects (or agents) can be defined and manipulated in a compositional manner, analogous to how linguistic structures are treated within the DisCoCat framework. The paper introduces a variety of spatial scenarios and relations such as Cartesian space, subway lines, and chessboards, highlighting the model's versatility in handling different spatial contexts.

Spatial Relations and Examples

In exploring spatial relations, the paper details how specific spatial scenarios (e.g., movement on a chessboard, the layout of a subway system) can be represented and reasoned about. An interesting aspect is how the model includes the distinct capabilities or properties of objects within a space (e.g., the movements available to different chess pieces) as part of its compositional structure. This is particularly evident in the examination of chessboard relations, where the interaction between the spatial positions of pieces and their potential moves are considered.

Concept Interaction and Extension into Broader Cognitive Domains

A compelling section of the paper discusses how the proposed model interacts with broader cognitive domains, such as the conceptual spaces theory introduced by Peter Gärdenfors. The authors suggest that the integration of linguistic structure with spatial reasoning could extend to other conceptual domains like color, smell, or taste. This prospect opens up fascinating avenues for research into how humans construct and communicate complex concepts that transcend mere spatial relationships.

Practical Implications and Future Developments

The research presented has significant implications for advancing natural language understanding and AI. By aligning spatial reasoning more closely with linguistic structure, AI systems could achieve more nuanced understanding and generation of language that involves spatial context. Looking forward, the authors speculate on extensions of their model to incorporate fuzzy logic and probabilistic reasoning, enabling even more sophisticated handling of spatially related language.

Concluding Remarks

Wang-Macianica and Coecke's work contributes to a deeper comprehension of the intersection between language and spatial cognition, illustrating the potential of compositional models in bridging these domains. Their exploratory approach invites further investigation into how such models can be extended to more complex spatial scenarios and integrated with other conceptual domains, posing intriguing possibilities for future explorations in AI and cognitive science.

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