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What Kind of Language Is Hard to Language-Model?

Mielke, Sabrina J., Cotterell, Ryan, Gorman, Kyle, Roark, Brian, Eisner, Jason·2019·Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
DOI 10.18653/v1/p19-1491
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  • Long Short-Term Memory

    Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318

  • Deep Contextualized Word Representations

    Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983

  • Stan : A Probabilistic Programming Language

    Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., & Riddell, A. (2017) · Journal of Statistical Software · doi:10.18637/jss.v076.i01 · Cited by 7,754 · cited 3×

    “Figure 6 shows the log-probability of held-out data under the regression model, by fixing the estimated difficulties d j (and sometimes also the estimated variance σ 2 ) to their values obtained from training data, and then finding either MAP estimates or posterior means (by running HMC using STAN) of 29 However, version…”
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Metadata

Authors
Mielke, S. J., Cotterell, R., Gorman, K., Roark, B., & Eisner, J.
Venue
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Publisher
Association for Computational Linguistics
Published
2019
Pages
4975-4989
DOI
10.18653/v1/p19-1491
Cited by
18
References
3

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