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Controlling Conditional Language Models without Catastrophic Forgetting.

, , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 11499-11528. PMLR, (2022)

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A Distributional Approach to Controlled Text Generation., , and . CoRR, (2020)Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages., , , , , , , , , and 37 other author(s). EMNLP (Findings), volume EMNLP 2020 of Findings of ACL, page 2144-2160. Association for Computational Linguistics, (2020)On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting., , , and . NeurIPS, (2022)Neural Wikipedian: Generating Textual Summaries from Knowledge Base Triples., , , , , , and . J. Web Semant., (2018)References in Wikipedia: The Editors' Perspective., and . WWW (Companion Volume), page 535-538. ACM / IW3C2, (2021)Self-Supervised and Controlled Multi-Document Opinion Summarization., , , and . CoRR, (2020)A Fully Automated Approach for Arabic Slang Lexicon Extraction from Microblogs., and . CICLing (1), volume 8403 of Lecture Notes in Computer Science, page 79-91. Springer, (2014)What Language Model to Train if You Have One Million GPU Hours?, , , , , , , , , and 8 other author(s). EMNLP (Findings), page 765-782. Association for Computational Linguistics, (2022)Controlling Conditional Language Models without Catastrophic Forgetting., , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 11499-11528. PMLR, (2022)A Distributional Approach to Controlled Text Generation., , and . ICLR, OpenReview.net, (2021)