[16] J. Hale. A probabilistic earley parser as a psycholinguistic model. In Second meeting of the north american chapter of the association for computational linguistics, 2001.
[17] J. Hale. Information-theoretical complexity metrics. Language and Linguistics Compass, 10(9): 397-412, 2016.
[18] T. B. Hashimoto, H. Zhang, and P. Liang. Unifying human and statistical evaluation for natural language generation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1689-1701, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics. doi: 10.18653/v1/N19-1169. URL https://aclanthology.org/N19-1169.
[19] A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi. The curious case of neural text degeneration. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum?id=rygGQyrFvH.
[20] T. F. Jaeger. Redundancy and reduction: Speakers manage syntactic information density. Cognitive psychology, 61(1):23-62, 2010.
[21] I. Kaplan. Dft of a non-stationary time series, Sep 2001. URL http://bearcave.com/misl/misl_tech/signal/nonstat/index.html.
[22] F. A. Kruse, A. Lefkoff, J. Boardman, K. Heidebrecht, A. Shapiro, P. Barloon, and A. Goetz. The spectral image processing system (sips)—interactive visualization and analysis of imaging spectrometer data. Remote sensing of environment, 44(2-3):145–163, 1993.
[23] T. Kynkänniemi, T. Karras, S. Laine, J. Lehtinen, and T. Aila. Improved precision and recall metric for assessing generative models. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019. URL https://proceedings.neurips.cc/paper_files/paper/2019/file/0234c510bc6d908b28c70ff313743079-Paper.pdf.
[24] R. Levy. Expectation-based syntactic comprehension. Cognition, 106(3):1126-1177, 2008.
[25] R. Levy and T. Jaeger. Speakers optimize information density through syntactic reduction. In B. Schölkopf, J. Platt, and T. Hofmann, editors, Advances in Neural Information Processing Systems, volume 19, pages 849-856, 2007.
[26] X. L. Li, A. Holtzman, D. Fried, P. Liang, J. Eisner, T. Hashimoto, L. Zettlemoyer, and M. Lewis. Contrastive decoding: Open-ended text generation as optimization. arXiv preprint arXiv:2210.15097, 2022.
[27] S. Merity, C. Xiong, J. Bradbury, and R. Socher. Pointer sentinel mixture models. In International Conference on Learning Representations, 04 2017.
[28] O. Oullier, G. C. De Guzman, K. J. Jantzen, J. Lagarde, and J. Scott Kelso. Social coordination dynamics: Measuring human bonding. *Social neuroscience*, 3(2):178–192, 2008.
[29] K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 311-318, Philadelphia, Pennsylvania, USA, July 2002. Association for Computational Linguistics. doi: 10.3115/1073083.1073135. URL https://aclanthology.org/P02-1040.
[30] S. Piantadosi. Zipf's word frequency law in natural language: A critical review and future directions. Psychonomic bulletin & review, 21, 03 2014. doi: 10.3758/s13423-014-0585-6.
[31] K. Pillutla, S. Swayamdipta, R. Zellers, J. Thickstun, S. Welleck, Y. Choi, and Z. Harchaoui. Mauve: Measuring the gap between neural text and human text using divergence frontiers. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, and J. W. Vaughan, editors, Advances in Neural Information Processing Systems, volume 34, pages 4816-4828. Curran Associates, Inc., 2021. URL https://proceedings.neurips.cc/paper_files/paper/2021/file/260c2432a0eccc28ce03c10dadc078a4-Paper.pdf.