NLP领域必读的8篇论文_nlp论文

推荐下NLP领域内最重要的8篇论文吧（依据学术范标准评价体系得出的8篇名单）：

一、Deep contextualized word representations

作者：Matthew E. Peters / Mark Neumann / Mohit Iyyer / Matt Gardner / Christopher M. Clark / ... / Luke Zettlemoyer

摘要：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 of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals.

全文链接：Deep contextualized word representations——学术范

二、Glove: Global Vectors for Word Representation

作者：Piotr Bojanowski / Edouard Grave / Armand Joulin / Tomas Mikolov

摘要：Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related model