Performance in language modelling has been significantly improved by training recurrent neural networks on large corpora. This progress has come at the cost of interpretabil-ity and an understanding of how these architectures function, making principled development of better language models more difficult. We look inside a state-of-the-art neural language model to analyse how this model represents high-level lexico-semantic information. In particular, we investigate how the model represents words by extracting activation patterns where they occur in the text, and compare these representations directly to human semantic knowledge.
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