100篇必读的自然语言处理NLP论文

这是100篇重要的自然语言处理NLP)论文的列表,认真研究该领域的学生和研究人员可能应该了解和阅读。此列表由Masato Hagiwara编制。

无只是在同行评审的会议/期刊论文中发表论文。这里还包括教程/调查式论文和博客文章,这些文章通常比原始论文更容易理解。

这份清单远非完整或客观,而且随着时间推移发表的一些重要论文也会陆续补充进来。你可以通过拉取请求问题通知作者。

机器学习

  • 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.

神经模型

  • 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.

聚类和词/句子嵌入

  • 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.

主题模型

  • 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.

语言建模

  • 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.

分类,标记,解析

  • 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.

顺序标记和信息提取

  • 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.

机器翻译和音译,Seq2seq模型

  • 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.

共指解析

  • 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.

自动文本汇总

  • 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.

问答和机器理解

  • 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.

自动生成,强化学习

  • 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.

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