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Be Consistent! Improving Procedural Text Comprehension using Label Consistency.

, , , , , , and . NAACL-HLT (1), page 2347-2356. Association for Computational Linguistics, (2019)

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Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text., , , , and . CoRR, (2019)Interscript: A dataset for interactive learning of scripts through error feedback., , , , and . CoRR, (2021)Improving scripts with a memory of natural feedback., , , and . CoRR, (2021)Improving Neural Model Performance through Natural Language Feedback on Their Explanations., , , , , , and . CoRR, (2021)proScript: Partially Ordered Scripts Generation via Pre-trained Language Models., , , , , and . CoRR, (2021)CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization., , , , , , , and . CoRR, (2023)Learning to repair: Repairing model output errors after deployment using a dynamic memory of feedback., , , and . NAACL-HLT (Findings), page 339-352. Association for Computational Linguistics, (2022)Multimedia Data for the Visually Impaired., , and . AAAI, page 4210-4211. AAAI Press, (2015)Reasoning about Actions and State Changes by Injecting Commonsense Knowledge., , , , , and . EMNLP, page 57-66. Association for Computational Linguistics, (2018)A Dataset for Tracking Entities in Open Domain Procedural Text., , , , , , , and . EMNLP (1), page 6408-6417. Association for Computational Linguistics, (2020)