Recent artificial neural networks that process natural language achieve unprecedented performance in tasks requiring sentence-level understanding. As such, they could be interesting models of the integration of linguistic information in the human brain. We review works that compare these artificial language models with human brain activity and we assess the extent to which this approach has improved our understanding of the neural processes involved in natural language comprehension. Two main results emerge. First, the neural representation of word meaning aligns with the context-dependent, dense word vectors used by the artificial neural networks. Second, the processing hierarchy that emerges within artificial neural networks broadly matches the brain, but is surprisingly inconsistent across studies. We discuss current challenges in establishing artificial neural networks as process models of natural language comprehension. We suggest exploiting the highly structured representational geometry of artificial neural networks when mapping representations to brain data.

翻译：近期，处理自然语言的人工神经网络在需要句子级理解的任务中取得了前所未有的性能。因此，它们可以作为人脑中语言信息整合的有趣模型。我们回顾了将这些人工语言模型与人脑活动进行比较的研究，并评估了这种方法在多大程度上增进了我们对自然语言理解所涉及的神经过程的理解。主要得出两个结论。首先，词义的神经表征与人工神经网络使用的上下文相关、密集词向量一致。其次，人工神经网络内部出现的处理层级总体上与大脑相匹配，但在不同研究中却存在令人惊讶的不一致性。我们讨论了当前将人工神经网络确立为自然语言理解过程模型所面临的挑战。我们建议在将表征映射到脑数据时，利用人工神经网络高度结构化的表征几何特性。