形态学习、类型学和变化建模:神经序列到序列框架的贡献是什么?

Q2 Social Sciences

Journal of Language Modelling Pub Date : 2019-12-19 DOI:10.15398/jlm.v7i1.244

M. Elsner, Andrea D. Sims, Alexander Erdmann, A. Hernandez, Evan Jaffe, Lifeng Jin, Martha Booker Johnson, Shuan O. Karim, David L. King, Luana Lamberti Nunes, Byung-Doh Oh, Nathan Rasmussen, Cory Shain, Stephanie Antetomaso, Kendra V. Dickinson, N. Diewald, Michelle Mckenzie, S. Stevens-Guille

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引用次数: 11

摘要

我们调查了使用神经序列到序列模型作为形态学学习和可学习性计算模型的研究。我们讨论了它们在确定屈折指数的可预测性、对语言习得进行预测和语言变化建模方面的应用。最后，对今后的工作提出了建议。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Modeling morphological learning, typology, and change: What can the neural sequence-to-sequence framework contribute?

We survey research using neural sequence-to-sequence models as compu-tational models of morphological learning and learnability. We discusstheir use in determining the predictability of inflectional exponents, inmaking predictions about language acquisition and in modeling languagechange. Finally, we make some proposals for future work in these areas.

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来源期刊

Journal of Language Modelling Social Sciences-Linguistics and Language

CiteScore

1.30

自引率

0.00%

发文量

审稿时长

9 weeks