Awesome NLP Papers

This is a collection/reading-list of awesome Natural Language Processing papers sorted by date.

2018

• [X] Unsupervised Machine Translation Using Monolingual Corpora Only, Lample et al. Paper

• [X] On the Dimensionality of Word Embeddings, Yin et al. Paper

• [X] An efficient framework for learning sentence representations, Logeswaran et al. Paper

• [X] Refining Pretrained Word Embeddings Using Layer-wise Relevance Propagation, Akira Utsumi Paper

• [X] Domain Adapted Word Embeddings for Improved Sentiment Classification, Sarma et al. Paper

• [X] In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition, Sheikhshab et al. Paper

• [X] Generalizing Word Embeddings using Bag of Subwords, Zhao et al. Paper

• [X] What's in Your Embedding, And How It Predicts Task Performance, Rogers et al. Paper

• [X] On Learning Better Word Embeddings from Chinese Clinical Records: Study on Combining In-Domain and Out-Domain Data Wang et al. Paper

• [X] Predicting and interpreting embeddings for out of vocabulary words in downstream tasks, Garneau et al. Paper

• [X] Addressing Low-Resource Scenarios with Character-aware Embeddings, Papay et al. Paper

• [X] Domain Adaptation for Disease Phrase Matching with Adversarial Networks, Liu et al. Paper

• [X] Investigating Effective Parameters for Fine-tuning of Word Embeddings Using Only a Small Corpus, Komiya et al. Paper

• [X] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Devlin et al. Paper

• [X] Adapting Word Embeddings from Multiple Domains to Symptom Recognition from Psychiatric Notes, Zhang et al. Paper

• [ ] Evaluation of sentence embeddings in downstream and linguistic probing tasks, Perone et al. Paper

• [ ] Universal Sentence Encoder, Cer et al. Paper

• [X] Deep Contextualized Word Representations, Peters et al. Paper

• [X] Learned in Translation: Contextualized Word Vectors, McCann et al. Paper

• [X] Concatenated p-mean Word Embeddings as Universal Cross-Lingual Sentence Representations, Rücklé et al. paper

• [X] A Compressed Sensing View of Unsupervised Text Embeddings, Bag-Of-n-Grams, and LSTMs, Arora et al. Paper

2017:

• [X] Attention Is All You Need, Vaswani et al. Paper

• [X] Skip-Gram – Zipf + Uniform = Vector Additivity, Gittens et al. Paper

• [X] A Simple but Tough-to-beat Baseline for Sentence Embeddings, Arora et al. Paper

• [X] Fast and Accurate Entity Recognition with Iterated Dilated Convolutions, Strubell et al. Paper

• [X] Advances in Pre-Training Distributed Word Representations, Mikolov et al. Paper

• [X] Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets, Dror et al. Paper

2016:

• [X] Towards Universal Paraphrastic Sentence Embeddings, Wieting et al. Paper

• [X] Bag of Tricks for Efficient Text Classification, Joulin et al. Paper

• [X] Enriching Word Vectors with Subword Information, Bojanowski et al. Paper

• [X] Assessing the Corpus Size vs. Similarity Trade-off for Word Embeddings in Clinical NLP, Kirk Roberts Paper

• [X] How to Train Good Word Embeddings for Biomedical NLP, Chiu et al. Paper

• [X] Log-Linear Models, MEMMs, and CRFs, Michael Collins Paper

• [X] Counter-fitting Word Vectors to Linguistic Constraints, Mrkšić et al. Paper

• [X] Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, Wu et al. Paper

2015:

• [ ] Semi-supervised Sequence Learning, Dai et al. Paper

• [X] Evaluating distributed word representations for capturing semantics of biomedical concepts, Th et al. Paper

2014:

• [X] GloVe: Global Vectors for Word Representation, Pennington et al. Paper

• [X] Linguistic Regularities in Sparse and Explicit Word Representations, Levy and Goldberg. Paper

• [X] Neural Word Embedding as Implicit Matrix Factorization, Levy and Goldberg. Paper

• [X] word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method, Goldberg and Levy. Paper

• [X] What’s in a p-value in NLP?, Søgaard et al. Paper

• [X] How transferable are features in deep neural networks?, Yosinski et al. Paper

• [X] Improving lexical embeddings with semantic knowledge, Yu et al. Paper

• [X] Retrofitting word vectors to semantic lexicons, Faruqui et al. Paper

2013:

• [X] Efficient Estimation of Word Representations in Vector Space, Mikolov et al. Paper

• [X] Linguistic Regularities in Continuous Space Word Representations, Mikolov et al. Paper

• [X] Distributed Representations of Words and Phrases and their Compositionality, Mikolov et al. Paper

2012:

• [X] An Empirical Investigation of Statistical Significance in NLP, Berg-Kirkpatrick et al. Paper

2010:

• [X] Word representations: A simple and general method for semi-supervised learning, Turian et al. Paper

2008:

• [ ] A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning, Collobert and Weston. Paper

2006:

• [X] Domain adaptation with structural correspondence learning, Blitzer et al. Paper

2003:

• [X] A Neural Probabilistic Language Model, Bengio et al. Paper

1986:

• [ ] Distributed Representations, Hinton et al. Paper