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Natural Language Generation (NLG), and more generally generative AI, are among the currently most impactful research fields.
Deep-speare: A joint neural model of poetic language, meter and rhyme
Jey Han Lau, Trevor Cohn, Timothy Baldwin, Julian Brooke, and Adam Hammond. 2018 · 1958
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Type/token ratios: What do they really tell us?
Brian Richards. 1987 · 1987
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Pattern matching: The gestalt approach
John W Ratcliff, David Metzener, et al. 1988 · 1988
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Color indexing
Michael J Swain and Dana H Ballard. 1991 · 1991
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Handbook of creativity
Robert J Sternberg. 1999 · 1999
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An expert system for the composition of formal spanish poetry
Pablo Gervás. 2001 · 2001
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Squibs and discussions: Human variation and lexical choice
Ehud Reiter and Somayajulu Sripada. 2002 · 2002
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An assessment of the range and usefulness of lexical diversity measures and the potential of the measure of textual, lexical diversity (MTLD)
Philip M McCarthy. 2005 · 2005
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Squibs and discussions: Real versus template-based natural language generation: A false opposition?
Kees van Deemter, Emiel Krahmer, and Mariët Theune. 2005 · 2005
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Evaluation of text generation: A survey
Asli Celikyilmaz, Elizabeth Clark, and Jianfeng Gao. 2020 · 2006
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Avoiding repetition in generated text
Mary Ellen Foster and Michael White. 2007 · 2007
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Cutting the gordian knot: The moving-average type–token ratio (mattr)
Michael A Covington and Joe D McFall. 2010 · 2010
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Computational creativity: The final frontier?
Simon Colton, Geraint A Wiggins, et al. 2012 · 2012
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
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CAN: creative adversarial networks, generating "art" by learning about styles and deviating from style norms
Ahmed M. Elgammal, Bingchen Liu, Mohamed Elhoseiny, and Marian Mazzone. 2017 · 2017
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Automatically generating rhythmic verse with neural networks
Jack Hopkins and Douwe Kiela. 2017 · 2017
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Survey of the state of the art in natural language generation: Core tasks, applications and evaluation
Albert Gatt and Emiel Krahmer. 2018 · 2018
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Measuring the diversity of automatic image descriptions
Emiel van Miltenburg, Desmond Elliott, and Piek Vossen. 2018 · 2018
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Generating informative and diverse conversational responses via adversarial information maximization
Yizhe Zhang, Michel Galley, Jianfeng Gao, Zhe Gan, Xiujun Li, Chris Brockett, and Bill Dolan. 2018 · 2018
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Boosting dialog response generation
Wenchao Du and Alan W Black. 2019 · 2019
Cited alongside, same era.
Unifying human and statistical evaluation for natural language generation
Tatsunori B. Hashimoto, Hugh Zhang, and Percy Liang. 2019 · 2019
Cited alongside, same era.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
Comparison of diverse decoding methods from conditional language models
Daphne Ippolito, Reno Kriz, João Sedoc, Maria Kustikova, and Chris Callison-Burch. 2019 · 2019
Cited alongside, same era.
Learning rhyming constraints using structured adversaries
Harsh Jhamtani, Sanket Vaibhav Mehta, Jaime G Carbonell, and Taylor Berg-Kirkpatrick. 2019 · 2019
Cited alongside, same era.
Hotel scribe: Generating high variation hotel descriptions
Saad Mahamood and Maciej Zembrzuski. 2019 · 2019
Evaluating the evaluation of diversity in natural language generation
Guy Tevet and Jonathan Berant. 2021 · 2021
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End-to-end style-conditioned poetry generation: What does it take to learn from examples alone?
Jörg Wöckener, Thomas Haider, Tristan Miller, The-Khang Nguyen, Thanh Tung Linh Nguyen, Minh Vu Pham, Jonas Belouadi, and Steffen Eger. 2021 · 2021
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Bartscore: Evaluating generated text as text generation
Weizhe Yuan, Graham Neubig, and Pengfei Liu. 2021 · 2021
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Decoding, fast and slow: A case study on balancing trade-offs in incremental, character-level pragmatic reasoning
Sina Zarrieß, Hendrik Buschmeier, Ting Han, and Simeon Schüz. 2021 · 2021
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GPT-NeoX-20B: An open-source autoregressive language model
Sidney Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, Usvsn Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, and Samuel Weinbach. 2022 · 2022
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Cited alongside, same era.
A robot’s street credibility: Modeling authenticity judgments for artificially generated hip-hop lyrics
Enrique Manjavacas, Mike Kestemont, and Folgert Karsdorp. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Cited alongside, same era.
Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Cited alongside, same era.
MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance
Wei Zhao, Maxime Peyrard, Fei Liu, Yang Gao, Christian M. Meyer, and Steffen Eger. 2019 · 2019
Cited alongside, same era.
Acrostic poem generation
Rajat Agarwal and Katharina Kann. 2020 · 2020
Cited alongside, same era.
Language gans falling short
Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, and Laurent Charlin. 2020 · 2020
Cited alongside, same era.
Help me write a poem - instruction tuning as a vehicle for collaborative poetry writing
Tuhin Chakrabarty, Vishakh Padmakumar, and He He. 2022 · 2022
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Reproducibility issues for BERT-based evaluation metrics
Yanran Chen, Jonas Belouadi, and Steffen Eger. 2022 · 2022
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Semantic diversity in dialogue with natural language inference
Katherine Stasaski and Marti Hearst. 2022 · 2022
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On decoding strategies for neural text generators
Gian Wiher, Clara Meister, and Ryan Cotterell. 2022 · 2022
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ByGPT5: End-to-end style-conditioned poetry generation with token-free language models
Jonas Belouadi and Steffen Eger. 2023 · 2023
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Missing information, unresponsive authors, experimental flaws: The impossibility of assessing the reproducibility of previous human evaluations in NLP
Anya Belz, Craig Thomson, and Ehud Reiter. 2023 · 2023
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MENLI: Robust Evaluation Metrics from Natural Language Inference
Yanran Chen and Steffen Eger. 2023 · 2023
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On the creativity of large language models
Giorgio Franceschelli and Mirco Musolesi. 2023 · 2023
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The Eval4NLP 2023 shared task on prompting large language models as explainable metrics
Christoph Leiter, Juri Opitz, Daniel Deutsch, Yang Gao, Rotem Dror, and Steffen Eger. 2023 · 2023
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How much do language models copy from their training data? evaluating linguistic novelty in text generation using RAVEN
R. Thomas McCoy, Paul Smolensky, Tal Linzen, Jianfeng Gao, and Asli Celikyilmaz. 2023 · 2023
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Can ai-generated text be reliably detected?
Vinu Sankar Sadasivan, Aounon Kumar, S. Balasubramanian, Wenxiao Wang, and Soheil Feizi. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023 · 2023
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Llama 3 model card
AI@Meta. 2024 · 2024
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