A collection/reading-list of awesome Natural Language Processing papers sorted by date.
This is a collection/reading-list of awesome Natural Language Processing papers sorted by date.
Unsupervised Machine Translation Using Monolingual Corpora Only, Lample et al.
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On the Dimensionality of Word Embeddings, Yin et al.
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An efficient framework for learning sentence representations, Logeswaran et al.
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Refining Pretrained Word Embeddings Using Layer-wise Relevance Propagation, Akira Utsumi
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Domain Adapted Word Embeddings for Improved Sentiment Classification, Sarma et al.
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In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition, Sheikhshab et al.
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Generalizing Word Embeddings using Bag of Subwords, Zhao et al.
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What's in Your Embedding, And How It Predicts Task Performance, Rogers et al.
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On Learning Better Word Embeddings from Chinese Clinical Records: Study on Combining In-Domain and Out-Domain Data Wang et al.
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Predicting and interpreting embeddings for out of vocabulary words in downstream tasks, Garneau et al.
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Addressing Low-Resource Scenarios with Character-aware Embeddings, Papay et al.
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Domain Adaptation for Disease Phrase Matching with Adversarial Networks, Liu et al.
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Investigating Effective Parameters for Fine-tuning of Word Embeddings Using Only a Small Corpus, Komiya et al.
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Devlin et al.
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Adapting Word Embeddings from Multiple Domains to Symptom Recognition from Psychiatric Notes, Zhang et al.
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Evaluation of sentence embeddings in downstream and linguistic probing tasks, Perone et al.
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Universal Sentence Encoder, Cer et al.
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Deep Contextualized Word Representations, Peters et al.
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Learned in Translation: Contextualized Word Vectors, McCann et al.
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Concatenated p-mean Word Embeddings as Universal Cross-Lingual Sentence Representations, Rücklé et al.
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A Compressed Sensing View of Unsupervised Text Embeddings, Bag-Of-n-Grams, and LSTMs, Arora et al.
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Attention Is All You Need, Vaswani et al.
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Skip-Gram – Zipf + Uniform = Vector Additivity, Gittens et al.
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A Simple but Tough-to-beat Baseline for Sentence Embeddings, Arora et al.
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Fast and Accurate Entity Recognition with Iterated Dilated Convolutions, Strubell et al.
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Advances in Pre-Training Distributed Word Representations, Mikolov et al.
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Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets, Dror et al.
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Towards Universal Paraphrastic Sentence Embeddings, Wieting et al.
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Bag of Tricks for Efficient Text Classification, Joulin et al.
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Enriching Word Vectors with Subword Information, Bojanowski et al.
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Assessing the Corpus Size vs. Similarity Trade-off for Word Embeddings in Clinical NLP, Kirk Roberts
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How to Train Good Word Embeddings for Biomedical NLP, Chiu et al.
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Log-Linear Models, MEMMs, and CRFs, Michael Collins
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Counter-fitting Word Vectors to Linguistic Constraints, Mrkšić et al.
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Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, Wu et al.
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Semi-supervised Sequence Learning, Dai et al.
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Evaluating distributed word representations for capturing semantics of biomedical concepts, Th et al.
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GloVe: Global Vectors for Word Representation, Pennington et al.
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Linguistic Regularities in Sparse and Explicit Word Representations, Levy and Goldberg.
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Neural Word Embedding as Implicit Matrix Factorization, Levy and Goldberg.
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word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method, Goldberg and Levy.
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What’s in a p-value in NLP?, Søgaard et al.
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How transferable are features in deep neural networks?, Yosinski et al.
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Improving lexical embeddings with semantic knowledge, Yu et al.
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Retrofitting word vectors to semantic lexicons, Faruqui et al.
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Efficient Estimation of Word Representations in Vector Space, Mikolov et al.
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Linguistic Regularities in Continuous Space Word Representations, Mikolov et al.
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Distributed Representations of Words and Phrases and their Compositionality, Mikolov et al.
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