An Ensembled Encoder-Decoder System for Interlinear Glossed Text

Edith Coates
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引用次数: 1

Abstract

This paper presents my submission to Track 1 of the 2023 SIGMORPHON shared task on interlinear glossed text (IGT). There are a wide amount of techniques for building and training IGT models (see Moeller and Hulden, 2018; McMillan-Major, 2020; Zhao et al., 2020). I describe my ensembled sequence-to-sequence approach, perform experiments, and share my submission’s test-set accuracy. I also discuss future areas of research in low-resource token classification methods for IGT.
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行间有光文本的集成编码器-解码器系统
本文介绍了我对2023年SIGMORPHON关于行间光滑文本(IGT)共享任务的第1轨道的提交。有大量的技术用于构建和训练IGT模型(参见Moeller和Hulden, 2018;McMillan-Major, 2020;赵等,2020)。我描述了我的集成序列到序列方法,进行了实验,并分享了我提交的测试集的准确性。我还讨论了IGT的低资源令牌分类方法的未来研究领域。
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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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