用词作为分类器统一形式、分布与具身语义，探索神经符号AI新路径。
Could the Road to Grounded, Neuro-symbolic AI be Paved with Words-as-Classifiers?
- 提出以'词即分类器'为桥梁，融合形式、分布与具身语义理论。
- 通过认知科学依据与对话实验验证该模型在语义理解中的有效性。
- 适合关注多模态语义建模与神经符号系统融合的研究者。
形式化、分布与具身的计算语义理论各有优劣。当前语言模型趋向引入视觉知识以实现具身化，同时呼吁融合符号方法以兼顾三类理论的优势。本文主张，'词即分类器'模型或可成为统一三大语义范式的可行路径。该模型已在形式系统与分布语言模型中得到应用，并在交互对话场景中经过充分验证。我们综述相关文献，结合近期认知科学成果论证其合理性，并设计小型实验加以支持。最后，提出一个基于词即分类器的统一语义框架。
原文摘要 · Abstract (English)
Formal, Distributional, and Grounded theories of computational semantics each have their uses and their drawbacks. There has been a shift to ground models of language by adding visual knowledge, and there has been a call to enrich models of language with symbolic methods to gain the benefits from formal, distributional, and grounded theories. In this paper, we attempt to make the case that one potential path forward in unifying all three semantic fields is paved with the words-as-classifier model, a model of word-level grounded semantics that has been incorporated into formalisms and distributional language models in the literature, and it has been well-tested within interactive dialogue settings. We review that literature, motivate the words-as-classifiers model with an appeal to recent work in cognitive science, and describe a small experiment. Finally, we sketch a model of semantics unified through words-as-classifiers.
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