给大家推荐100篇重要的NLP论文，论文列表来自于Quora上的一个问题的答案：哪些是所有NLP学生必须阅读的最重要的研究论文?这里的论文清单远不完整，只包括的非常经典的论文。希望对大家学习NLP有所帮助。Machine Learning
Avrim Blum and Tom Mitchell: Combining Labeled and Unlabeled Data with Co-Training, 1998.
John Lafferty, Andrew McCallum, Fernando C.N. Pereira: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, ICML 2001.
Charles Sutton, Andrew McCallum. An Introduction to Conditional Random Fields for Relational Learning.
Kamal Nigam, et al.: Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 1999.
Kevin Knight: Bayesian Inference with Tears, 2009.
Marco Tulio Ribeiro et al.: “Why Should I Trust You?”: Explaining the Predictions of Any Classifier, KDD 2016.
Marco Tulio Ribeiro et al.: Beyond Accuracy: Behavioral Testing of NLP Models with CheckList, ACL 2020.
Neural Models
Richard Socher, et al.: Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection, NIPS 2011.Ronan Collobert et al.: Natural Language Processing (almost) from Scratch, J. of Machine Learning Research, 2011.Richard Socher, et al.: Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank, EMNLP 2013.Xiang Zhang, Junbo Zhao, and Yann LeCun: Character-level Convolutional Networks for Text Classification, NIPS 2015.Yoon Kim: Convolutional Neural Networks for Sentence Classification, 2014.Christopher Olah: Understanding LSTM Networks, 2015.Matthew E. Peters, et al.: Deep contextualized word representations, 2018.Jacob Devlin, et al.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2018.Yihan Liu et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach, 2020.Clustering & Word/Sentence Embeddings
Peter F Brown, et al.: Class-Based n-gram Models of Natural Language, 1992.Tomas Mikolov, et al.: Efficient Estimation of Word Representations in Vector Space, 2013.Tomas Mikolov, et al.: Distributed Representations of Words and Phrases and their Compositionality, NIPS 2013.Quoc V. Le and Tomas Mikolov: Distributed Representations of Sentences and Documents, 2014.Jeffrey Pennington, et al.: GloVe: Global Vectors for Word Representation, 2014.Ryan Kiros, et al.: Skip-Thought Vectors, 2015.Piotr Bojanowski, et al.: Enriching Word Vectors with Subword Information, 2017.Daniel Cer et al.: Universal Sentence Encoder, 2018.Topic Models
Thomas Hofmann: Probabilistic Latent Semantic Indexing, SIGIR 1999.David Blei, Andrew Y. Ng, and Michael I. Jordan: Latent Dirichlet Allocation, J. Machine Learning Research, 2003.Language Modeling
Joshua Goodman: A bit of progress in language modeling, MSR Technical Report, 2001.Stanley F. Chen and Joshua Goodman: An Empirical Study of Smoothing Techniques for Language Modeling, ACL 2006.Yee Whye Teh: A Hierarchical Bayesian Language Model based on Pitman-Yor Processes, COLING/ACL 2006.Yee Whye Teh: A Bayesian interpretation of Interpolated Kneser-Ney, 2006.Yoshua Bengio, et al.: A Neural Probabilistic Language Model, J. of Machine Learning Research, 2003.Andrej Karpathy: The Unreasonable Effectiveness of Recurrent Neural Networks, 2015.Yoon Kim, et al.: Character-Aware Neural Language Models, 2015.Alec Radford, et al.: Language Models are Unsupervised Multitask Learners, 2018.Segmentation, Tagging, Parsing
Donald Hindle and Mats Rooth. Structural Ambiguity and Lexical Relations, Computational Linguistics, 1993.Adwait Ratnaparkhi: A Maximum Entropy Model for Part-Of-Speech Tagging, EMNLP 1996.Eugene Charniak: A Maximum-Entropy-Inspired Parser, NAACL 2000.Michael Collins: Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms, EMNLP 2002.Dan Klein and Christopher Manning: Accurate Unlexicalized Parsing, ACL 2003.Dan Klein and Christopher Manning: Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency, ACL 2004.Joakim Nivre and Mario Scholz: Deterministic Dependency Parsing of English Text, COLING 2004.Ryan McDonald et al.: Non-Projective Dependency Parsing using Spanning-Tree Algorithms, EMNLP 2005.Daniel Andor et al.: Globally Normalized Transition-Based Neural Networks, 2016.Oriol Vinyals, et al.: Grammar as a Foreign Language, 2015.
Sequential Labeling & Information Extraction
Marti A. Hearst: Automatic Acquisition of Hyponyms from Large Text Corpora, COLING 1992.Collins and Singer: Unsupervised Models for Named Entity Classification, EMNLP 1999.Patrick Pantel and Dekang Lin, Discovering Word Senses from Text, SIGKDD, 2002.Mike Mintz et al.: Distant supervision for relation extraction without labeled data, ACL 2009.Zhiheng Huang et al.: Bidirectional LSTM-CRF Models for Sequence Tagging, 2015.Xuezhe Ma and Eduard Hovy: End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF, ACL 2016.
Machine Translation & Transliteration, Sequence-to-Sequence Models
Peter F. Brown et al.: A Statistical Approach to Machine Translation, Computational Linguistics, 1990.Kevin Knight, Graehl Jonathan. Machine Transliteration. Computational Linguistics, 1992.Dekai Wu: Inversion Transduction Grammars and the Bilingual Parsing of Parallel Corpora, Computational Linguistics, 1997.Kevin Knight: A Statistical MT Tutorial Workbook, 1999.Kishore Papineni, et al.: BLEU: a Method for Automatic Evaluation of Machine Translation, ACL 2002.Philipp Koehn, Franz J Och, and Daniel Marcu: Statistical Phrase-Based Translation, NAACL 2003.Philip Resnik and Noah A. Smith: The Web as a Parallel Corpus, Computational Linguistics, 2003.Franz J Och and Hermann Ney: The Alignment-Template Approach to Statistical Machine Translation, Computational Linguistics, 2004.David Chiang. A Hierarchical Phrase-Based Model for Statistical Machine Translation, ACL 2005.Ilya Sutskever, Oriol Vinyals, and Quoc V. Le: Sequence to Sequence Learning with Neural Networks, NIPS 2014.Oriol Vinyals, Quoc Le: A Neural Conversation Model, 2015.Dzmitry Bahdanau, et al.: Neural Machine Translation by Jointly Learning to Align and Translate, 2014.Minh-Thang Luong, et al.: Effective Approaches to Attention-based Neural Machine Translation, 2015.Rico Sennrich et al.: Neural Machine Translation of Rare Words with Subword Units. ACL 2016.Yonghui Wu, et al.: Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, 2016.Melvin Johnson, et al.: Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation, 2016.Jonas Gehring, et al.: Convolutional Sequence to Sequence Learning, 2017.Ashish Vaswani, et al.: Attention Is All You Need, 2017.
Coreference Resolution
Vincent Ng: Supervised Noun Phrase Coreference Research: The First Fifteen Years, ACL 2010.Kenton Lee at al.: End-to-end Neural Coreference Resolution, EMNLP 2017.Automatic Text Summarization
Kevin Knight and Daniel Marcu: Summarization beyond sentence extraction. Artificial Intelligence 139, 2002.James Clarke and Mirella Lapata: Modeling Compression with Discourse Constraints. EMNLP-CONLL 2007.Ryan McDonald: A Study of Global Inference Algorithms in Multi-Document Summarization, ECIR 2007.Wen-tau Yih et al.: Multi-Document Summarization by Maximizing Informative Content-Words. IJCAI 2007.Alexander M Rush, et al.: A Neural Attention Model for Sentence Summarization. EMNLP 2015.Abigail See et al.: Get To The Point: Summarization with Pointer-Generator Networks. ACL 2017.Question Answering and Machine Comprehension
Pranav Rajpurkar et al.: SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP 2015.Minjoon Soo et al.: Bi-Directional Attention Flow for Machine Comprehension. ICLR 2015.Generation, Reinforcement Learning
Jiwei Li, et al.: Deep Reinforcement Learning for Dialogue Generation, EMNLP 2016.Marc’Aurelio Ranzato et al.: Sequence Level Training with Recurrent Neural Networks. ICLR 2016.Samuel R Bowman et al.: Generating sentences from a continuous space, CoNLL 2016.Lantao Yu, et al.: SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient, AAAI 2017.
