标题:头脑和机器中的单词含义

摘要】由于自然语言处理(NLP)的最新进展,机器显示出越来越广泛的语言能力。许多算法源于心理学领域过去的计算工作,提出了一个问题,即他们是否像人们一样理解单词。在本文中,我们比较了人类和机器如何表示单词的含义。我们认为,当代的NLP系统是人类单词相似性的有前途的模型,但是在许多其他方面都存在不足。当前的模型与大型语料库中基于文本的模式之间的联系过于紧密,而与人们使用单词进行表达的欲望,目标和信念之间的联系过于薄弱。单词的含义还必须以视觉和行动为基础,并且必须能够灵活组合,而当前系统却不能。对于开发具有更像人类的词义概念基础的机器,我们提出了具体的挑战。我们还将讨论对认知科学和自然语言处理的意义。

Title: Word meaning in minds and machines

[abstract]  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.

【作者】Brenden M. Lake, Gregory L. Murphy

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