神经多语形态屈折的训练策略

Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology Pub Date : 1900-01-01 DOI:10.18653/v1/2021.sigmorphon-1.26

Adam Ek, Jean-Philippe Bernardy

{"title":"神经多语形态屈折的训练策略","authors":"Adam Ek, Jean-Philippe Bernardy","doi":"10.18653/v1/2021.sigmorphon-1.26","DOIUrl":null,"url":null,"abstract":"This paper presents the submission of team GUCLASP to SIGMORPHON 2021 Shared Task on Generalization in Morphological Inflection Generation. We develop a multilingual model for Morphological Inflection and primarily focus on improving the model by using various training strategies to improve accuracy and generalization across languages.","PeriodicalId":187165,"journal":{"name":"Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Training Strategies for Neural Multilingual Morphological Inflection\",\"authors\":\"Adam Ek, Jean-Philippe Bernardy\",\"doi\":\"10.18653/v1/2021.sigmorphon-1.26\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the submission of team GUCLASP to SIGMORPHON 2021 Shared Task on Generalization in Morphological Inflection Generation. We develop a multilingual model for Morphological Inflection and primarily focus on improving the model by using various training strategies to improve accuracy and generalization across languages.\",\"PeriodicalId\":187165,\"journal\":{\"name\":\"Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18653/v1/2021.sigmorphon-1.26\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/2021.sigmorphon-1.26","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

本文介绍了GUCLASP团队向SIGMORPHON 2021提交的关于形态屈折生成泛化的共享任务。我们开发了一个多语言的形态学变形模型，并主要关注通过使用各种训练策略来改进模型，以提高跨语言的准确性和泛化性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Training Strategies for Neural Multilingual Morphological Inflection

This paper presents the submission of team GUCLASP to SIGMORPHON 2021 Shared Task on Generalization in Morphological Inflection Generation. We develop a multilingual model for Morphological Inflection and primarily focus on improving the model by using various training strategies to improve accuracy and generalization across languages.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

自引率

0.00%

发文量