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Generalization In NLI: Ways (not) To Go Beyond Simple Heuristics

Prajjwal Bhargava, Aleksandr Drozd, Anna Rogers . Proceedings of the Second Workshop on Insights from Negative Results in NLP 2021 – 70 citations

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Compositional Generalization Datasets Model Architecture

Much 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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