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Deep contextualized word representations
[article]
2018
arXiv
pre-print
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state
arXiv:1802.05365v2
fatcat:4fxzi2utynh25iqgx36lp5sila