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In neural network models of language, words are commonly represented usingcontext-invariant representations (word embeddings) which are then put incontext in the hidden layers. Since words are often ambiguous, representing thecontextually relevant information is not trivial. We investigate how an LSTMlanguage model deals with lexical ambiguity in English, designing a method toprobe its hidden representations for lexical and contextual information aboutwords. We find that both types of information are represented to a largeextent, but also that there is room for improvement for contextual information.

Putting words in context: LSTM language models and lexical ambiguity