{"title":"使用多语言转换模型的字素到音素转换","authors":"Omnia S. ElSaadany, Benjamin Suter","doi":"10.18653/v1/2020.sigmorphon-1.7","DOIUrl":null,"url":null,"abstract":"In this paper, we describe our three submissions to the SIGMORPHON 2020 shared task 1 on grapheme-to-phoneme conversion for 15 languages. We experimented with a single multilingual transformer model. We observed that the multilingual model achieves results on par with our separately trained monolingual models and is even able to avoid a few of the errors made by the monolingual models.","PeriodicalId":186158,"journal":{"name":"Special Interest Group on Computational Morphology and Phonology Workshop","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Grapheme-to-Phoneme Conversion with a Multilingual Transformer Model\",\"authors\":\"Omnia S. ElSaadany, Benjamin Suter\",\"doi\":\"10.18653/v1/2020.sigmorphon-1.7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we describe our three submissions to the SIGMORPHON 2020 shared task 1 on grapheme-to-phoneme conversion for 15 languages. We experimented with a single multilingual transformer model. We observed that the multilingual model achieves results on par with our separately trained monolingual models and is even able to avoid a few of the errors made by the monolingual models.\",\"PeriodicalId\":186158,\"journal\":{\"name\":\"Special Interest Group on Computational Morphology and Phonology Workshop\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Special Interest Group on Computational Morphology and Phonology Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18653/v1/2020.sigmorphon-1.7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Special Interest Group on Computational Morphology and Phonology Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/2020.sigmorphon-1.7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Grapheme-to-Phoneme Conversion with a Multilingual Transformer Model
In this paper, we describe our three submissions to the SIGMORPHON 2020 shared task 1 on grapheme-to-phoneme conversion for 15 languages. We experimented with a single multilingual transformer model. We observed that the multilingual model achieves results on par with our separately trained monolingual models and is even able to avoid a few of the errors made by the monolingual models.
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