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Found 2 papers, 2 papers with code
Supervising Model Attention with Human Explanations for Robust Natural Language Inference
Natural Language Inference (NLI) models are known to learn from biases and artefacts within their training data, impacting how well they generalise to other unseen datasets.
Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training
Natural Language Inference (NLI) datasets contain annotation artefacts resulting in spurious correlations between the natural language utterances and their respective entailment classes.
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