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Despite alarm over the reliance of machine learning systems on so-called spurious patterns, the term lacks coherent meaning in standard statistical frameworks.
Generating natural language under pragmatic constraints
Eduard Hovy · 1987
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Modeling annotators: A generative approach to learning from annotator rationales
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Rectified linear units improve restricted boltzmann machines
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Learning word vectors for sentiment analysis
Andrew L Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts · 2011
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Scikit-learn: Machine learning in Python
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A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning · 2015
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Adam: A method for stochastic optimization
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A thorough examination of the cnn/daily mail reading comprehension task
Danqi Chen, Jason Bolton, and Christopher D Manning · 2016
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Adversarial examples for evaluating reading comprehension systems
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Darling or babygirl? investigating stylistic bias in sentiment analysis
Judy Hanwen Shen, Lauren Fratamico, Iyad Rahwan, and Alexander M Rush · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
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How much reading does reading comprehension require? a critical investigation of popular benchmarks
Divyansh Kaushik and Zachary C Lipton · 2018
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Examining gender and race bias in two hundred sentiment analysis systems
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Deep contextualized word representations
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