{"title":"OUTLINE OF A DIDACTIC FRAMEWORK FOR COMBINED DATA LITERACY AND MACHINE TRANSLATION LITERACY TEACHING","authors":"Ralph Krüger, Janiça Hackenbuchner","doi":"10.51287/cttl202211","DOIUrl":null,"url":null,"abstract":"This paper outlines a didactic framework for combined data literacy and machine translation (MT) literacy teaching for translation and specialised communication students. The framework is being developed in the context of the DataLitMT project, a publicly funded project at the Institute of Translation and Multilingual Communication at TH Köln – University of Applied Sciences, Germany, which develops didactic resources for teaching data literacy in its translation-specific form of MT literacy to students of BA and MA programmes in translation and specialised communication studies. After discussing the high relevance of machine trans-lation literacy and data literacy in professional translation contexts, the paper introduces the DataLitMT project and discusses a framework of professional MT literacy and an MT-oriented data literacy framework, which form the two theoretical pillars of the project. Also, the interface between MT literacy and data literacy will be illustrated by showing how specific (sub)dimensions of data literacy can be mapped to relevant (sub)dimensions of professional MT literacy. Finally, the paper presents some preliminary didactic resources of the DataLitMT project – concerned with social biases in MT, with MT training data preparation and with automatic MT quality evaluation – and discusses how these resources can be used to teach specific (sub)dimensions of data literacy and professional MT literacy to students in the fields of translation/specialised communication studies. Keywords: data literacy, professional machine translation literacy, neural machine translation, translation didactics, DataLitMT","PeriodicalId":40810,"journal":{"name":"Current Trends in Translation Teaching and Learning E","volume":" ","pages":""},"PeriodicalIF":0.5000,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Trends in Translation Teaching and Learning E","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.51287/cttl202211","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"LINGUISTICS","Score":null,"Total":0}
引用次数: 2
Abstract
This paper outlines a didactic framework for combined data literacy and machine translation (MT) literacy teaching for translation and specialised communication students. The framework is being developed in the context of the DataLitMT project, a publicly funded project at the Institute of Translation and Multilingual Communication at TH Köln – University of Applied Sciences, Germany, which develops didactic resources for teaching data literacy in its translation-specific form of MT literacy to students of BA and MA programmes in translation and specialised communication studies. After discussing the high relevance of machine trans-lation literacy and data literacy in professional translation contexts, the paper introduces the DataLitMT project and discusses a framework of professional MT literacy and an MT-oriented data literacy framework, which form the two theoretical pillars of the project. Also, the interface between MT literacy and data literacy will be illustrated by showing how specific (sub)dimensions of data literacy can be mapped to relevant (sub)dimensions of professional MT literacy. Finally, the paper presents some preliminary didactic resources of the DataLitMT project – concerned with social biases in MT, with MT training data preparation and with automatic MT quality evaluation – and discusses how these resources can be used to teach specific (sub)dimensions of data literacy and professional MT literacy to students in the fields of translation/specialised communication studies. Keywords: data literacy, professional machine translation literacy, neural machine translation, translation didactics, DataLitMT