VarDial@COLING 2018最新文献

筛选
英文 中文
Encoder-Decoder Methods for Text Normalization 文本规范化的编码器-解码器方法
VarDial@COLING 2018 Pub Date : 2018-08-20 DOI: 10.5167/UZH-156775
M. Lusetti, T. Ruzsics, A. Göhring, T. Samardžić, E. Stark
{"title":"Encoder-Decoder Methods for Text Normalization","authors":"M. Lusetti, T. Ruzsics, A. Göhring, T. Samardžić, E. Stark","doi":"10.5167/UZH-156775","DOIUrl":"https://doi.org/10.5167/UZH-156775","url":null,"abstract":"Text normalization is the task of mapping non-canonical language, typical of speech transcription and computer-mediated communication, to a standardized writing. It is an up-stream task necessary to enable the subsequent direct employment of standard natural language processing tools and indispensable for languages such as Swiss German, with strong regional variation and no written standard. Text normalization has been addressed with a variety of methods, most successfully with character-level statistical machine translation (CSMT). In the meantime, machine translation has changed and the new methods, known as neural encoder-decoder (ED) models, resulted in remarkable improvements. Text normalization, however, has not yet followed. A number of neural methods have been tried, but CSMT remains the state-of-the-art. In this work, we normalize Swiss German WhatsApp messages using the ED framework. We exploit the flexibility of this framework, which allows us to learn from the same training data in different ways. In particular, we modify the decoding stage of a plain ED model to include target-side language models operating at different levels of granularity: characters and words. Our systematic comparison shows that our approach results in an improvement over the CSMT state-of-the-art.","PeriodicalId":431809,"journal":{"name":"VarDial@COLING 2018","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123231151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 38
Twist Bytes - German Dialect Identification with Data Mining Optimization 扭曲字节-德语方言识别与数据挖掘优化
VarDial@COLING 2018 Pub Date : 2018-08-01 DOI: 10.21256/ZHAW-4850
F. Souza, Ralf Grubenmann, Pius von Däniken, Dirk Von Gruenigen, Jan Deriu, Mark Cieliebak
{"title":"Twist Bytes - German Dialect Identification with Data Mining Optimization","authors":"F. Souza, Ralf Grubenmann, Pius von Däniken, Dirk Von Gruenigen, Jan Deriu, Mark Cieliebak","doi":"10.21256/ZHAW-4850","DOIUrl":"https://doi.org/10.21256/ZHAW-4850","url":null,"abstract":"We describe our approaches used in the German Dialect Identification (GDI) task at the VarDial Evaluation Campaign 2018. The goal was to identify to which out of four dialects spoken in German speaking part of Switzerland a sentence belonged to. We adopted two different meta classifier approaches and used some data mining insights to improve the preprocessing and the meta classifier parameters. Especially, we focused on using different feature extraction methods and how to combine them, since they influenced very differently the performance of the system. Our system achieved second place out of 8 teams, with a macro averaged F-1 of 64.6%.","PeriodicalId":431809,"journal":{"name":"VarDial@COLING 2018","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124472199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信