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Personalized text generation is an emerging research area that has attracted much attention in recent years.
Personalized response generation via generative split memory network. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . 1956–1970
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Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) . 1532–1543
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Style transfer from non-parallel text by cross-alignment
Tianxiao Shen, Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2017 · 2017
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Integrating reading and writing: supporting students’ writing from source
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Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) . 1865–1874
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Training Millions of Personalized Dialogue Agents. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . 2775–2779
Pierre-Emmanuel Mazare, Samuel Humeau, Martin Raison, and Antoine Bordes. 2018 · 2018
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ForumSum: A multi-speaker conversation summarization dataset. In Findings of the Association for Computational Linguistics: EMNLP 2021 . 4592–4599
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GeDi: Generative Discriminator Guided Sequence Generation. In Findings of the Association for Computational Linguistics: EMNLP 2021 . 4929–4952
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DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) . 6691–6706
Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A Smith, and Yejin Choi. 2021 · 2021
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Is the author recognition test a useful metric for native and non-native English speakers? An item response theory analysis
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Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) . 129–140
Sudha Rao and Joel Tetreault. 2018 · 2018
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Adafactor: Adaptive learning rates with sublinear memory cost. In International Conference on Machine Learning . PMLR, 4596–4604
Noam Shazeer and Mitchell Stern. 2018 · 2018
Cited alongside, same era.
Style transfer as unsupervised machine translation
Zhirui Zhang, Shuo Ren, Shujie Liu, Jianyong Wang, Peng Chen, Mu Li, Ming Zhou, and Enhong Chen. 2018b · 2018
Cited alongside, same era.
Abstractive Summarization of Reddit Posts with Multi-level Memory Networks. In NAACL-HLT
Byeongchang Kim, Hyunwoo Kim, and Gunhee Kim. 2019 · 2019
Cited alongside, same era.
Towards Controllable and Personalized Review Generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . 3237–3245
Pan Li and Alexander Tuzhilin. 2019 · 2019
Cited alongside, same era.
Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP) . 188–197
Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019 · 2019
Cited alongside, same era.
Knowledge-enhanced personalized review generation with capsule graph neural network. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 735–744
Junyi Li, Siqing Li, Wayne Xin Zhao, Gaole He, Zhicheng Wei, Nicholas Jing Yuan, and Ji-Rong Wen. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
Large dual encoders are generalizable retrievers
Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Ji Ma, Vincent Y Zhao, Yi Luan, Keith B Hall, Ming-Wei Chang, et al · 2021
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Pchatbot: A large-scale dataset for personalized chatbot. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2470–2477
Hongjin Qian, Xiaohe Li, Hanxun Zhong, Yu Guo, Yueyuan Ma, Yutao Zhu, Zhanliang Liu, Zhicheng Dou, and Ji-Rong Wen. 2021 · 2021
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FUDGE: Controlled Text Generation With Future Discriminators. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . 3511–3535
Kevin Yang and Dan Klein. 2021 · 2021
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Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models. In Findings of the Association for Computational Linguistics: ACL 2022 . 1864–1874
Jianmo Ni, Gustavo Hernandez Abrego, Noah Constant, Ji Ma, Keith Hall, Daniel Cer, and Yinfei Yang. 2022 · 2022
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022 · 2022
Later among the works it cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Later among the works it cites.
Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . 5808–5820
Hanxun Zhong, Zhicheng Dou, Yutao Zhu, Hongjin Qian, and Ji-Rong Wen. 2022 · 2022
Later among the works it cites.
Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al · 2023
Closest in time.
LaMP: When Large Language Models Meet Personalization
Alireza Salemi, Sheshera Mysore, Michael Bendersky, and Hamed Zamani. 2023 · 2023
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