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Free/Open-Source Machine Translation for the Low-Resource Languages of Spain (Invited Talk) 西班牙低资源语言的免费/开源机器翻译(特邀演讲)

International Conference on Language, Data, and Knowledge Pub Date : 1900-01-01 DOI: 10.4230/OASIcs.LDK.2021.3

M. Forcada

{"title":"Free/Open-Source Machine Translation for the Low-Resource Languages of Spain (Invited Talk)","authors":"M. Forcada","doi":"10.4230/OASIcs.LDK.2021.3","DOIUrl":"https://doi.org/10.4230/OASIcs.LDK.2021.3","url":null,"abstract":"While machine translation has historically been rule-based, that is, based on dictionaries and rules written by experts, most present-day machine translation is corpus-based. In the last few years, statistical machine translation, the dominant corpus-based approach, has been displaced by neural machine translation in most applications, in view of the better results reported, particularly for languages with very different syntax. But both statistical and neural machine translation need to be trained on large amounts of parallel data, that is, sentences in one language carefully paired with their translations in their other language, and this is a resource that may not be available for some low-resource languages. While some of the languages of Spain may be considered to be reasonably endowed with parallel corpora connecting them to Spanish or even to English - Basque, Catalan, Galician -, and are well-served with machine translation systems, there are many other languages which cannot afford them such as Aranese Occitan, Aragonese, or Asturian/Leonese. Fortunately, languages in this last group belong to the Romance language family, as Spanish does, and this makes translation from and into Spanish under a rule-based paradigm the only feasible approach. After describing briefly the main machine translation paradigms, I will describe the Apertium free/open-source rule-based machine translation platform, which has been used to build machine translation systems for these low-resource languages of Spain, indeed, sometimes the only ones available. The free/open-source setting has made linguistic data for these languages available for anyone in their linguistic communities to build other linguistic technologies for these low-resourced languages. For example, the Apertium family of bilingual and monolingual data has been converted into RDF and they have been made accessible on the Web as linked data.","PeriodicalId":377119,"journal":{"name":"International Conference on Language, Data, and Knowledge","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122562473","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: 0

An Ontology for CoNLL-RDF: Formal Data Structures for TSV Formats in Language Technology CoNLL-RDF本体:语言技术中TSV格式的形式化数据结构

International Conference on Language, Data, and Knowledge Pub Date : 1900-01-01 DOI: 10.4230/OASIcs.LDK.2021.20

C. Chiarcos, Maxim Ionov, Luis Glaser, Christian Fäth

{"title":"An Ontology for CoNLL-RDF: Formal Data Structures for TSV Formats in Language Technology","authors":"C. Chiarcos, Maxim Ionov, Luis Glaser, Christian Fäth","doi":"10.4230/OASIcs.LDK.2021.20","DOIUrl":"https://doi.org/10.4230/OASIcs.LDK.2021.20","url":null,"abstract":"In language technology and language sciences, tab-separated values (TSV) represent a frequently used formalism to represent linguistically annotated natural language, often addressed as \"CoNLL formats\". A large number of such formats do exist, but although they share a number of common features, they are not interoperable, as different pieces of information are encoded differently in these dialects. \u0000CoNLL-RDF refers to a programming library and the associated data model that has been introduced to facilitate processing and transforming such TSV formats in a serialization-independent way. CoNLL-RDF represents CoNLL data, by means of RDF graphs and SPARQL update operations, but so far, without machine-readable semantics, with annotation properties created dynamically on the basis of a user-defined mapping from columns to labels. Current applications of CoNLL-RDF include linking between corpora and dictionaries [Mambrini and Passarotti, 2019] and knowledge graphs [Tamper et al., 2018], syntactic parsing of historical languages [Chiarcos et al., 2018; Chiarcos et al., 2018], the consolidation of syntactic and semantic annotations [Chiarcos and Fath, 2019], a bridge between RDF corpora and a traditional corpus query language [Ionov et al., 2020], and language contact studies [Chiarcos et al., 2018]. \u0000We describe a novel extension of CoNLL-RDF, introducing a formal data model, formalized as an ontology. The ontology is a basis for linking RDF corpora with other Semantic Web resources, but more importantly, its application for transformation between different TSV formats is a major step for providing interoperability between CoNLL formats.","PeriodicalId":377119,"journal":{"name":"International Conference on Language, Data, and Knowledge","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114075543","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: 5

Improving Intent Detection Accuracy Through Token Level Labeling 通过令牌级标注提高意图检测的准确性

International Conference on Language, Data, and Knowledge Pub Date : 1900-01-01 DOI: 10.4230/OASIcs.LDK.2021.30

Michal Lew, Aleksander Obuchowski, Monika Kutyła

引用次数: 2

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