日本語 English
| 開講年度/ Academic YearAcademic Year |
20232023 |
| 科目設置学部/ CollegeCollege |
異文化コミュニケーション研究科/Graduate School of Intercultural CommunicationGraduate School of Intercultural Communication |
| 科目コード等/ Course CodeCourse Code |
VV306/VV306VV306 |
| テーマ・サブタイトル等/ Theme・SubtitleTheme・Subtitle |
Theoretical and Practical Foundation for Advanced Technologies in Translation and Interpretation |
| 授業形態/ Class FormatClass Format |
対面（一部オンライン）/Face-to-face (partially online)Face-to-face (partially online) |
| 校地/ CampusCampus |
池袋/IkebukuroIkebukuro |
| 学期/ SemesterSemester |
春学期/Spring SemesterSpring Semester |
| 曜日時限・教室/ DayPeriod・RoomDayPeriod・Room |
木5･8502/Thu.5･8502 Thu.5･8502 |
| 単位/ CreditCredit |
22 |
| 科目ナンバリング/ Course NumberCourse Number |
ICC6243 |
| 使用言語/ LanguageLanguage |
その他/OthersOthers |
| 備考/ NotesNotes |
|
| テキスト用コード/ Text CodeText Code |
VV306 |
This course aims at equipping students with basic knowledge and experiences of technologies utilized by linguists such as professional translators and project managers. The technologies to be covered in the course are Neural Machine Translation, including customization of it, post-editing and pre-editing tourniquets, orthodox CAT tools such as translation memory, terminology management tools, subtitling tools. Through the course, students are also expected to learn how to use technologies for research purposes, techniques such as corpus analysis, text mining, translation process research data collection methods.
The course consists of a) understanding of the basic mechanism of AI-related NMT such as RNN, Transformer, word embedding, b) mastering CAT tools including translation memory, terminology management tools, including effective practice in post-editing and pre-editing, c) being familiar with customizing machine translation, including domain adaptation and corpus data cleansing, d) being knowledgeable of project management and localization tool. The theme also covers post-editing, controlled language, speech recognition, data analyses, and privacy protection.
Throughout the course, students will be asked to read relevant articles, join a discussion, and complete assignments as specified. This course will be interactive and participative, in which students will be requested to study actively each subject and reflect what they learn onto their translation projects to be assigned in classes.
※Please refer to Japanese Page for details including evaluations, textbooks and others.
This course aims at equipping students with basic knowledge and experiences of technologies utilized by linguists such as professional translators and project managers. The technologies to be covered in the course are Neural Machine Translation, including customization of it, post-editing and pre-editing tourniquets, orthodox CAT tools such as translation memory, terminology management tools, subtitling tools. Through the course, students are also expected to learn how to use technologies for research purposes, techniques such as corpus analysis, text mining, translation process research data collection methods.
The course consists of a) understanding of the basic mechanism of AI-related NMT such as RNN, Transformer, word embedding, b) mastering CAT tools including translation memory, terminology management tools, including effective practice in post-editing and pre-editing, c) being familiar with customizing machine translation, including domain adaptation and corpus data cleansing, d) being knowledgeable of project management and localization tool. The theme also covers post-editing, controlled language, speech recognition, data analyses, and privacy protection.
Throughout the course, students will be asked to read relevant articles, join a discussion, and complete assignments as specified. This course will be interactive and participative, in which students will be requested to study actively each subject and reflect what they learn onto their translation projects to be assigned in classes.
| 1 | Introduction |
| 2 | CAT tools 1 |
| 3 | CAT tools 2 |
| 4 | Post-editing 1 |
| 5 | Post-editing 2 |
| 6 | Machine translation 1: History |
| 7 | Machine translation 2 Theory |
| 8 | Machine translation 3: Domain adaptation |
| 9 | Terminology management and Project Management |
| 10 | Data cleansing, data mining, corpus analysis |
| 11 | Data-driven research and translation process research 1 |
| 12 | Data-driven research and translation process research 2 |
| 13 | Total workbench environment |
| 14 | Review |
Reading assignments will be given respectively. Students will be asked to join discussions on the assigned articles.
| 種類 (Kind) | 割合 (%) | 基準 (Criteria) |
|---|---|---|
| 平常点 (In-class Points) | 100 |
Reflection paper(20%) Homework assignments(30%) Project-based assignments(50%) |
| 備考 (Notes) | ||
| その他 (Others) | |||||
|---|---|---|---|---|---|
| Articles and handouts will be provided in class. |
| No | 著者名 (Author/Editor) | 書籍名 (Title) | 出版社 (Publisher) | 出版年 (Date) | ISBN/ISSN |
|---|---|---|---|---|---|
| 1 | Minako O'Hagan | The Routledge Handbook of Translation and Technology | Routledge | 2019 | 9781138232846 |
| 2 | 坂西優・山田優 | 『自動翻訳大全』 | 三才ブックス | 2020 | 4866731931 |
| 3 | ティエリー・ポイボー, 中澤 敏明 | 『機械翻訳:歴史・技術・産業』 | 森北出版 | 2020 | 4627851812 |
https://researchmap.jp/yamada_trans
全授業回のうち3回のみオンライン実施（実施回については「Canvas LMS」で指示する）。