  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.
