We propose two probabilistic methods to build models that are more robust to such biases and better transfer across datasets. Don’t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference Yonatan Belinkov*, Adam Poliak*, Stuart M. Shieber, Benjamin Van Durme, Alexander Rush. Don’t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference Yonatan Belinkov*, Adam Poliak*, Stuart Shieber, Benjamin Van Durme, Alexander Rush Related: TFIDF [1905.06221] Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets [1907.04380] Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference [1906.09635] Investigating Biases in Textual Entailment Datasets [1805.01042] Hypothesis Only Baselines in Natural Language Inference [1805.02266] Breaking NLI … Natural Language Inference (NLI) datasets often contain hypothesis-only biases---artifacts that allow models to achieve non-trivial performance without learning whether a premise entails a hypothesis. *SEM 2018 [5] Tsuchiya et al. LREC 2018 Hypothesis Only Baselines in Natural Language Inference. ACL. Or, discuss a change on Slack. [Media: Harvard News] On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Association for Computational Linguistics Florence, Italy conference publication Natural Language Inference (NLI) datasets often contain hypothesis-only biases—artifacts that allow models to achieve non-trivial performance without learning whether a premise entails a hypothesis.
Performance Impact Caused by Hidden Bias of Training Data for Recognizing Textual Entailment. [3] Belinkov & Poliak et al., Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference, ACL 2019 [4] Poliak et al. We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). You can create a new account if you don't have one.
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