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Deep Contextualized Word Representations

Proceedings of NAACL-HLT 2018, pages 2227 2237 New Orleans, Louisiana, June 1 - 6, 2018 Association for Computational LinguisticsDeep Contextualized word representationsMatthew E. Peters , Mark Neumann , Mohit Iyyer , Matt Gardner Clark , Kenton Lee , Luke Zettlemoyer Allen Institute for Artificial Intelligence Paul G. Allen School of Computer Science & Engineering, University of WashingtonAbstractWe introduce a new type ofdeep contextual-izedword representation that models both (1)complex characteristics of word use ( , syn-tax and semantics), and (2) how these usesvary across linguistic contexts ( , to modelpolysemy). Our word vectors are learned func-tions of the internal states of a deep bidirec-tional language model (biLM), which is pre-trained on a large text corpus.

the representations learned at the Þrst layer in a 2-layer LSTM encoder are better at predicting POS tags then second layer. Finally, the top layer of an LSTMforencodingwordcontext( Melamudetal. , 2016 ) has been shown to learn representations of word sense. We show that similar signals are also

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