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Paper notes

Notes from papers I'm reading, ordered chronologically.

NLP

  1. Conditional Random Fields: probabilistic models for segmenting and labeling sequence data, Lafferty et al, 2001 [Paper] [Notes] #nlp #architectures
  2. Introduction to the CoNLL-2003 shared task: language-independent named entity recognition, Sang et al., 2003 [Paper] [Notes] #nlp #datasets
  3. Bidirectional LSTM-CRF Models for sequence tagging, Huang et al., 2015 [Paper] [Notes] #nlp #architectures
  4. Neural Architectures for Named Entity Recognition, Lample et al., 2016 [Paper] [Notes] #nlp #architectures #NER
  5. Named Entity Recognition with Bidirectional LSTM-CNNs, Chiu et al., 2016 [Paper] [Notes] #nlp #architectures
  6. Semi-supervised sequence tagging with bidirectional language models, Peters et al., 2017 [Paper] [Notes] #nlp #embeddings
  7. Attention is all you need, Vaswani et al., 2018 [Paper] [Notes] #nlp #architectures
  8. Deep contextualized word representations, Peters et al., 2018 [Paper] [Notes] #nlp #embeddings
  9. Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable, Hangya et al., 2018 [Paper] [Notes] #nlp
  10. A Named Entity Recognition Shootout for German, Riedl and Padó, 2018 [Paper] [Notes] #nlp #NER #datasets
  11. SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference, Zellers et al., 2018 [Paper] [Notes] #nlp #datasets
  12. Dissecting contextual word embeddings: architecture and representation, Peters et al., 2018 [Paper] [Notes] #nlp #embeddings
  13. Contextual string embeddings for sequence labeling, Akbik et al., 2018 [Paper] [Notes] #nlp #embeddings
  14. Targeted synctactic evaluation of language models, Marvin and Linzen, 2018 [Paper] [Notes] #nlp #linguistics
  15. BERT: Pre-training of deep bidirectional transformers for language understanding, Devlin et al., 2018 [Paper] [Notes] #nlp #embeddings
  16. Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference., McCoy et al., 2019 [Paper] [Notes] #nlp #linguistics #datasets
  17. Linguistic Knowledge and Transferability of Contextual Representations, Liu et al., 2019 [Paper] [Notes] #nlp
  18. What do you learn from context? Probing for sentence structure in contextualized word representations, Tenney et al., 2019 [Paper] [Notes] #nlp
  19. HellaSwag: Can a Machine Really Finish Your Sentence?, Zellers et al., 2019 [Paper] [Notes] #nlp #datasets
  20. Flair: an easy-to-use framework for stat-of-the-art NLP [Paper] [Notes] #nlp #embeddings
  21. Towards Robust Named Entity Recognition for Historic German, Schweter et al., 2019 [Paper] [Notes] #nlp #NER
  22. XLNet: generalized autoregressive pretraining for language understanding, Yang et al., 2019 [Paper] [Notes] #nlp #architectures
  23. R-Transformer: Recurrent Neural Network Enhanced Transformer, Wang et al., 2019 [Paper] [Notes] #nlp #architectures
  24. Probing Neural Network Comprehension of Natural Language Arguments, Nivel and Kao, 2019 [Paper] [Notes] #nlp #datasets
  25. Language Models as Knowledge Bases?, Petroni et al., 2019 [Paper] [Notes] #nlp #linguistics
  26. HuggingFace's Transformers: State-of-the-art Natural Language Processing, Wolf et al., 2019 [Paper] [Notes] #nlp #frameworks
  27. Evaluating the Factual Consistency of Abstractive Text Summarization, Kryscinski et al., 2019 [Paper] [Notes] #nlp #text-summarization
  28. Generalization through Memorization: Nearest Neighbor Language Models, Khandelwal et al., 2019 [Paper] [Notes] #nlp #architectures
  29. Single Headed Attention RNN: Stop Thinking With Your Head, Merity, 2019 [Paper] [Notes] #nlp #architectures
  30. What’s Going On in Neural Constituency Parsers? An Analysis, Gaddy et al., 2018 [Paper] [Notes] #nlp
  31. BPE-Dropout: simple and effective subword regularization, Provilkov et al., 2019 [Paper] [Notes] #nlp

Embeddings

  1. Semi-supervised sequence tagging with bidirectional language models, Peters et al., 2017 [Paper] [Notes] #nlp #embeddings
  2. Deep contextualized word representations, Peters et al., 2018 [Paper] [Notes] #nlp #embeddings
  3. Dissecting contextual word embeddings: architecture and representation, Peters et al., 2018 [Paper] [Notes] #nlp #embeddings
  4. BERT: Pre-training of deep bidirectional transformers for language understanding, Devlin et al., 2018 [Paper] [Notes] #nlp #embeddings

Architectures

  1. Conditional Random Fields: probabilistic models for segmenting and labeling sequence data, Lafferty et al, 2001 [Paper] [Notes] #nlp #architectures
  2. Bidirectional LSTM-CRF Models for sequence tagging, Huang et al., 2015 [Paper] [Notes] #nlp #architectures
  3. Neural Architectures for Named Entity Recognition, Lample et al., 2016 [Paper] [Notes] #nlp #architectures #NER
  4. Named Entity Recognition with Bidirectional LSTM-CNNs, Chiu et al., 2016 [Paper] [Notes] #nlp #architectures
  5. Attention is all you need, Vaswani et al., 2018 [Paper] [Notes] #nlp #architectures
  6. Reasoning with Sarcasm by Reading In-between, Tay et al., 2018 [Paper] [Notes] #sarcasm-detection #architectures
  7. XLNet: generalized autoregressive pretraining for language understanding, Yang et al., 2019 [Paper] [Notes] #nlp #architectures
  8. R-Transformer: Recurrent Neural Network Enhanced Transformer, Wang et al., 2019 [Paper] [Notes] #nlp #architectures
  9. Generalization through Memorization: Nearest Neighbor Language Models, Khandelwal et al., 2019 [Paper] [Notes] #nlp #architectures
  10. Single Headed Attention RNN: Stop Thinking With Your Head, Merity, 2019 [Paper] [Notes] #nlp #architectures
  11. A Transformer-based approach to Irony and Sarcasm detection, Potamias et al., 2019 [Paper] [Notes] #sarcasm-detection #architecture

Frameworks

  1. Flair: an easy-to-use framework for stat-of-the-art NLP [Paper] [Notes] #nlp #frameworks
  2. HuggingFace's Transformers: State-of-the-art Natural Language Processing, Wolf et al., 2019 [Paper] [Notes] #nlp #frameworks
  3. Selective Brain Damage: Measuring the Disparate Impact of Model Pruning, Hooker et al., 2019 [Paper] [Notes] #frameworks

Datasets

  1. Introduction to the CoNLL-2003 shared task: language-independent named entity recognition, Sang et al., 2003 [Paper] [Notes] #nlp #datasets
  2. SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference, Zellers et al., 2018 [Paper] [Notes] #nlp #datasets
  3. A Named Entity Recognition Shootout for German, Riedl and Padó, 2018 [Paper] [Notes] #nlp #NER #datasets
  4. Probing Neural Network Comprehension of Natural Language Arguments, Nivel and Kao, 2019 [Paper] [Notes] #nlp #datasets
  5. Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference., McCoy et al., 2019 [Paper] [Notes] #nlp #linguistics #datasets
  6. UR-FUNNY: A Multimodal Language Dataset for Understanding Humor, Hasan et al., 2019 [Paper] [Notes] #sarcasm-detection #datasets
  7. HellaSwag: Can a Machine Really Finish Your Sentence?, Zellers et al., 2019 [Paper] [Notes] #nlp #datasets
  8. Sentiment analysis is not solved! Assessing and probing sentiment classification, Barnes et al., 2019 [Paper] [Notes] #nlp #datasets
  9. Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model, Cai et al., 2019 [Paper] [Notes] #sarcasm-detection #datasets
  10. Towards Multimodal Sarcasm Detection (An Obviously Perfect Paper), Castro et al., 2019 [Paper] [Notes] #sarcasm-detection #datasets
  11. iSarcasm: A Dataset of Intended Sarcasm, Oprea et al., 2019 [Paper] [Notes] #datasets #sarcasm-detection

NER

  1. Introduction to the CoNLL-2003 shared task: language-independent named entity recognition, Sang et al., 2003 [Paper] [Notes] #nlp #datasets #NER
  2. Neural Architectures for Named Entity Recognition, Lample et al., 2016 [Paper] [Notes] #nlp #architectures #NER
  3. Named Entity Recognition with Bidirectional LSTM-CNNs, Chiu et al., 2016 [Paper] [Notes] #nlp #architectures #NER
  4. Towards Robust Named Entity Recognition for Historic German, Schweter et al., 2019 [Paper] [Notes] #nlp #NER
  5. A Named Entity Recognition Shootout for German, Riedl and Padó, 2018 [Paper] [Notes] #nlp #NER #datasets

Sarcasm detection

summary

  1. Sarcasm Detection on Twitter: A Behavioral Modeling Approach, Rajadesingan et al., 2015 [Paper] [Notes] #sarcasm-detection
  2. Contextualized Sarcasm Detection on Twitter, Bamman and Smith, 2015 [Paper] [Notes] #sarcasm-detection
  3. Harnessing Context Incongruity for Sarcasm Detection, Joshi et al., 2015 [Paper] [Notes] #linguistics #sarcasm-detection
  4. Automatic Sarcasm Detection: A Survey, Joshi et al., 2017 [Paper] [Notes] #sarcasm-detection
  5. Detecting Sarcasm is Extremely Easy ;-), Parde and Nielsen, 2018 [Paper] [Notes] #sarcasm-detection
  6. CASCADE: Contextual Sarcasm Detection in Online Discussion Forums, Hazarika et al., 2018 [Paper] [Notes] #sarcasm-detection
  7. Reasoning with Sarcasm by Reading In-between, Tay et al., 2018 [Paper] [Notes] #sarcasm-detection #architectures
  8. Tweet Irony Detection with Densely Connected LSTM and Multi-task Learning, Wu et al., 2018 [Paper] [Notes] #sarcasm-detection
  9. UR-FUNNY: A Multimodal Language Dataset for Understanding Humor, Hasan et al., 2019 [Paper] [Notes] #sarcasm-detection #datasets
  10. Exploring Author Context for Detecting Intended vs Perceived Sarcasm, Oprea and Magdy, 2019 [Paper] [Notes] #sarcasm-detection
  11. Towards Multimodal Sarcasm Detection (An Obviously Perfect Paper), Castro et al., 2019 [Paper] [Notes] #sarcasm-detection #datasets
  12. Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model, Cai et al., 2019 [Paper] [Notes] #sarcasm-detection #datasets
  13. A2Text-Net: A Novel Deep Neural Network for Sarcasm Detection, Liu et al., 2019 [Paper] [Notes] #sarcasm-detection
  14. Sarcasm detection in tweets, Rajagopalan et al., 2019 [Paper] [Notes] #sarcasm-detection
  15. A Transformer-based approach to Irony and Sarcasm detection, Potamias et al., 2019 [Paper] [Notes] #sarcasm-detection #architecture
  16. Deep and dense sarcasm detection, Pelser et al., 2019 [Paper] [Notes] #sarcasm-detection
  17. iSarcasm: A Dataset of Intended Sarcasm, Oprea et al., 2019 [Paper] [Notes] #datasets #sarcasm-detection

Text summarization

  1. Evaluating the Factual Consistency of Abstractive Text Summarization, Kryscinski et al., 2019 [Paper] [Notes] #nlp #text-summarization

Reinforcement learning

  1. Mastering Atari, Go, Chess and Shogi by Planning with a learned model, Schrittwieser et al., 2019 [Paper] [Notes] #reinforcement-learning

Computer vision

  1. Cubic Stylization, Derek Liu and Jacobson, 2019 [Paper] [Notes] #computer-vision

Linguistics

  1. Harnessing Context Incongruity for Sarcasm Detection, Joshi et al., 2015 [Paper] [Notes] #linguistics #sarcasm-detection
  2. Targeted synctactic evaluation of language models, Marvin and Linzen, 2018 [Paper] [Notes] #nlp #linguistics
  3. Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference., McCoy et al., 2019 [Paper] [Notes] #nlp #linguistics #datasets
  4. Language Models as Knowledge Bases?, Petroni et al., 2019 [Paper] [Notes] #nlp #linguistics
  5. Different languages, similar encoding efficiency: Comparable information rates across the human communicative niche, Coupé et al., 2019 [Paper] [Notes] #linguistics #social-sciences

Social sciences

  1. How much does education improve intelligence? A meta-analysis, Ritchie et al., 2017 [Paper] [Notes] #social-sciences
  2. Fake news game confers psychological resistance against online misinformation, Roozenbeek and van der Linden, 2019 [Paper] [Notes] #social-sciences #humanities
  3. Different languages, similar encoding efficiency: Comparable information rates across the human communicative niche, Coupé et al., 2019 [Paper] [Notes] #linguistics #social-sciences
  4. Kids these days: Why the youth of today seem lacking, Protzko and Schooler, 2019 [Paper] [Notes] #social-sciences

Humanities

  1. Fake news game confers psychological resistance against online misinformation, Roozenbeek and van der Linden, 2019 [Paper] [Notes] #social-sciences #humanities

Physics

  1. First-order transition in a model of prestige bias, Skinner, 2019 [Paper] [Notes] #physics

Neuroscience

  1. A deep learning framework for neuroscience, Richard et al., 2019 [Paper] [Notes] #neuroscience

Algorithms

  1. Replace or Retrieve Keywords In Documents At Scale, Singh, 2017 [Paper] [Notes] #algorithms