Notes from papers I'm reading, ordered chronologically.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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