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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. We show thatthese Representations can be easily added toexisting models and significantly improve thestate of the art across six challenging NLPproblems, including question answering, tex-tual entailment and sentiment analysis.

learn a linear combination of the vectors stacked above each input word for each end task, which ... ers of deep biRNNs encode different types of in-formation. For example, introducing multi-task ... ing the previous token given the future context: p(t1,t2,...,tN)=!N k =1

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