The IMS–CUBoulder System for the SIGMORPHON 2020 Shared Task on Unsupervised Morphological Paradigm Completion

Manuel Mager, Katharina Kann
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引用次数: 2

Abstract

In this paper, we present the systems of the University of Stuttgart IMS and the University of Colorado Boulder (IMS--CUBoulder) for SIGMORPHON 2020 Task 2 on unsupervised morphological paradigm completion (Kann et al., 2020). The task consists of generating the morphological paradigms of a set of lemmas, given only the lemmas themselves and unlabeled text. Our proposed system is a modified version of the baseline introduced together with the task. In particular, we experiment with substituting the inflection generation component with an LSTM sequence-to-sequence model and an LSTM pointer-generator network. Our pointer-generator system obtains the best score of all seven submitted systems on average over all languages, and outperforms the official baseline, which was best overall, on Bulgarian and Kannada.
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无监督形态范式完成SIGMORPHON 2020共享任务的IMS-CUBoulder系统
在本文中,我们介绍了斯图加特大学IMS和科罗拉多大学博尔德分校(IMS- CUBoulder)用于SIGMORPHON 2020任务2的无监督形态范式完成的系统(Kann等人,2020)。该任务包括生成一组引理的形态范式,只给出引理本身和未标记的文本。我们建议的系统是与任务一起引入的基线的修改版本。特别是,我们尝试用LSTM序列到序列模型和LSTM指针生成器网络代替拐点生成组件。在所有语言中,我们的指针生成器系统在所有提交的七个系统中平均得分最高,并且在保加利亚语和卡纳达语方面的表现优于官方基准,而官方基准总体上是最好的。
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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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