2021
EMNLP
EMNLP 2021
Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics
Abstract
AbstractMuch of recent progress in NLU was shown to be due to models’ learning dataset-specific heuristics. We conduct a case study of generalization in NLI (from MNLI to the adversarially constructed HANS dataset) in a range of BERT-based architectures (adapters, Siamese Transformers, HEX debiasing), as well as with subsampling the data and increasing the model size. We report 2 successful and 3 unsuccessful strategies, all providing insights into how Transformer-based models learn to generalize.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— adversarial generalization
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Interpretability
Machine Learning > Learning Types > Adversarial Learning
Machine Learning > Learning Types > Weakly Supervised Learning
Deep Learning > Architectures > Transformers
Natural Language Processing > Applications > Text Classification
Natural Language Processing > Applications > Natural Language Inference
Machine Learning > Learning Types > Deep Learning
Deep Learning > Models > Transformers
Machine Learning > Learning Types > Generalization
Deep Learning > Learning Types > Generalization