Machines have achieved a broad and growing set of linguistic competencies,
thanks to recent progress in Natural Language Processing (NLP). Psychologists
have shown increasing interest in such models, comparing their output to
psychological judgments such as similarity, association, priming, and
comprehension, raising the question of whether the models could serve as
psychological theories. In this article, we compare how humans and machines
represent the meaning of words. We argue that contemporary NLP systems are
fairly successful models of human word similarity, but they fall short in many
other respects. Current models are too strongly linked to the text-based
patterns in large corpora, and too weakly linked to the desires, goals, and
beliefs that people express through words. Word meanings must also be grounded
in perception and action and be capable of flexible combinations in ways that
current systems are not. We discuss more promising approaches to grounding NLP
systems and argue that they will be more successful with a more human-like,
conceptual basis for word meaning.

本文对比了人类和机器如何表现单词的意义，认为自然语言处理系统在某些方面可以作为人类单词相似性的成功模型，但在其他方面还有不足。当前的模型过度关注大型语料库中基于文本的模式，而与人们通过单词表达的愿望、目标和信念联系过于弱。我们讨论了更有前途的基于视觉和行动本质的自然语言处理系统，并认为这些系统将更成功地实现更类似于人的概念基础单词意义。