Machines show an increasingly broad set of linguistic competencies, thanks to recent progress in Natural Language Processing (NLP). Many algorithms stem from past computational work in psychology, raising the question of whether they understand words as people do. In this paper, we compare how humans and machines represent the meaning of words. We argue that contemporary NLP systems are promising 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 use words in order to express. Word meanings must also be grounded in vision and action, and capable of flexible combinations, in ways that current systems are not. We pose concrete challenges for developing machines with a more human-like, conceptual basis for word meaning. We also discuss implications for cognitive science and NLP.
翻译:由于最近在自然语言处理(NLP)方面的进展,机器表现出了一套越来越广泛的语言能力。许多算法源自过去的心理学计算工作,提出了他们是否像人一样理解文字的问题。在本文中,我们比较了人类和机器如何代表文字的含义。我们争辩说,当代NLP系统是人类词汇相似的有希望的模式,但在许多其他方面却不尽人意。目前的模型与大公司基于文字的模式过于紧密地联系在一起,而且与人们用文字表达的愿望、目标和信念联系太弱。 语言的含义也必须建立在视觉和行动上,并且能够灵活地结合,其方式与当前系统不同。我们对开发具有更像人类的、概念基础的文字含义的机器提出了具体的挑战。我们还讨论了认知科学和NLP对认知科学的影响。