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Do Massively Pretrained Language Models Make Better Storytellers?

, , , , and . CoNLL, page 843-861. Association for Computational Linguistics, (2019)

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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics., , , , , , , , , and 45 other author(s). CoRR, (2021)Don't Take This Out of Context! On the Need for Contextual Models and Evaluations for Stylistic Rewriting., , and . CoRR, (2023)Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling., , , and . EMNLP (Findings), page 3367-3378. Association for Computational Linguistics, (2022)Do Massively Pretrained Language Models Make Better Storytellers?, , , , and . CoNLL, page 843-861. Association for Computational Linguistics, (2019)Don't Take This Out of Context!: On the Need for Contextual Models and Evaluations for Stylistic Rewriting., , , and . EMNLP, page 11419-11444. Association for Computational Linguistics, (2023)Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase., , and . EACL, page 1852-1860. Association for Computational Linguistics, (2021)COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements., , , , , , and . ACL (Findings), page 6294-6315. Association for Computational Linguistics, (2023)Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language., , , , , and . EMNLP (Findings), page 9759-9777. Association for Computational Linguistics, (2023)