EHU at the SIGMORPHON 2016 Shared Task. A Simple Proposal: Grapheme-to-Phoneme for Inflection

I. Alegria, Izaskun Etxeberria
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引用次数: 10

Abstract

This paper presents a proposal for learning morphological inflections by a graphemeto-phoneme learning model. No special processing is used for specific languages. The starting point has been our previous research on induction of phonology and morphology for normalization of historical texts. The results show that a very simple method can indeed improve upon some baselines, but does not reach the accuracies of the best systems in the task.
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EHU在SIGMORPHON 2016共享任务大会上。一个简单的建议:字母到音素的屈折
本文提出了一种基于字形-音素学习模型的形态屈折学习方法。没有对特定语言进行特殊处理。本文的出发点是我们之前对历史文本的语音和形态学归纳法的研究。结果表明,一种非常简单的方法确实可以提高一些基线,但不能达到任务中最佳系统的精度。
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Colexifications for Bootstrapping Cross-lingual Datasets: The Case of Phonology, Concreteness, and Affectiveness KU-CST at the SIGMORPHON 2020 Task 2 on Unsupervised Morphological Paradigm Completion Linguist vs. Machine: Rapid Development of Finite-State Morphological Grammars Exploring Neural Architectures And Techniques For Typologically Diverse Morphological Inflection SIGMORPHON 2020 Task 0 System Description: ETH Zürich Team
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