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研究生: 唐瑄
Tang, Hsuan
論文名稱: 翻譯研究所學生使用機器翻譯之意圖與接受度初探—以全臺翻譯研究所學生為例
Exploring Student Translators' Acceptance and Intention to Use Machine Translation: A Case Study of Translation Students in Taiwan
指導教授: 汝明麗
Ju, Ming-Li
口試委員: 廖柏森
Liao, Posen
陳碧珠
Chen, Pearl
汝明麗
Ju, Ming-Li
口試日期: 2022/01/26
學位類別: 碩士
Master
系所名稱: 翻譯研究所
Graduate Institute of Translation and Interpretation
論文出版年: 2022
畢業學年度: 110
語文別: 中文
論文頁數: 153
中文關鍵詞: 機器翻譯翻譯教學科技接受度模型
英文關鍵詞: Machine Translation, T&I Training, Technology Acceptance Model (TAM)
DOI URL: http://doi.org/10.6345/NTNU202200388
論文種類: 學術論文
相關次數: 點閱:181下載:44
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  • 近年來機器翻譯與類神經技術的結合與發展,在翻譯產業掀起了一陣波瀾,改變了翻譯產業的生態及譯者工作的模式。鑑於機器翻譯與譯者工作的連結愈來愈緊密,產業的相關需求也不斷提升(Slator, 2021; DePalma et al., 2021),許多翻譯學者(Mellinger, 2017)紛紛呼籲,翻譯教育應納入翻譯科技能力的相關訓練,以確保學生在未來自動化科技發展的浪潮下,仍能維持專業譯者的市場競爭力。

    本研究以全臺九所授予翻譯碩士學位學校之學生為研究對象,以Davis(1989)提出之科技接受度模型(Technology Acceptance Model)為基礎,結合過去相關研究實證之外部變項,採問卷調查法並結合半結構式訪談,初探目前翻譯人才的機器翻譯使用與接受度現況,並試圖探討影響學生機器翻譯使用與接受度的關鍵因素,分析當前各大翻譯學校(碩士學位層級)提供的訓練如何影響學生的機器翻譯使用與態度。

    本研究問卷於2021年10月至11月進行發放,共回收79份有效問卷,並自問卷受訪者中選擇10位進行半結構式訪談。資料經統計分析所得結果如下:

    (一) 高達98.73%的受訪學生具有機器翻譯使用經驗。
    (二) 受訪學生的機器翻譯使用意圖(即接受度)非常高。
    (三) 知覺有用為影響受訪學生機器翻譯接受度的關鍵因素,而知覺有用又顯著受知覺易用、工作關聯等外部變項影響。
    (四) 信任及對機器翻譯的恐懼會接影響受訪學生的機器翻譯使用意圖。
    (五) 機器翻譯相關教課程對受訪學生的機器翻譯接受度無正向影響,惟具備電腦輔助翻譯工具訓練經驗者,機器翻譯接受度則顯著高於其餘受訪者。

    研究結果顯示,全臺翻譯相關系所(碩士層級)學生的機器翻譯使用頻率與接受度皆非常高,而科技接受度模型也驗證了許多影響其接受度高低的關鍵因素,針對目前全臺翻譯相關系所(碩士層級)開設之機器翻譯相關課程所面臨的侷限,提供了實質建議與未來發展方向。

    In recent years, machine translation's (MT) integration with artificial neural networks has sparked discussions within the translation industry. The unprecedented advancement in MT technology has transformed the industry dynamics as well as how translators work.

    In view of MT's increasing impact on translation practices and the industry's increasing demands in MT-related services (Slator, 2021; DePalma et al., 2021), translation scholars (Mellinger, 2017) have called on translation institutes to formalize machine translation training in an effort to ensure professional translators' competitiveness in a future increasingly dominated by automation.
    This study aims to explore the use and acceptance of MT, as well as the key factors behind the attitudes and intention to use among translation students enrolled in the Translation and Interpretation (T&I) master's programs across Taiwan through an extended Technology Acceptance Model (TAM) and semi-structured interviews. The model used in this study is based on Davis's (1989) Technology Acceptance Model and is incorporated with potential factors that were found significant in previous studies. Through locating the key factors, this study seeks to understand how the trainings currently offered at the T&I institutes affects translation students' attitude and intention to use MT in Taiwan.

    A total of 79 surveys were collected from 9 T&I schools in Taiwan between October to November, 2021; and 10 survey respondents were selected for semi-structured interviews. The key findings were as follows:

    1. 98.73% of the translation students surveyed have experience using MT.
    2. The translation students' intention to use (or acceptance of) MT is rather strong.
    3. Perceived usefulness is the key factor behind translation students' intention to use MT; and translation students' perceived usefulness is significantly affected by perceived ease of use, job relevance and other external factors.
    4. Trust of quality and fear of MT's growing influence directly affects translation students' intention to use MT.
    5. MT-related trainings offered at the T&I schools do not have a positive effect on translation students' intention to use MT. Though, translation students with Computer-Aided Translation Tools (CAT Tools) training scored significantly higher in their intention to use MT than those without any CTA Tools training.

    The results showed that the frequency and acceptance of MT among translation students in Taiwan are rather high. The proposed TAM model successfully validated a number of key factors behind their acceptance of MT. In response to the current constraints of the MT-related trainings in Taiwan, the results indeed shed light on the possible enhancements of the future trainings at the T&I institutes.

    第壹章 研究背景 1 第一節 科技帶動的全球產業變革 1 第二節 筆譯產業與機器翻譯 1 第三節 翻譯教育與機器翻譯 3 第四節 研究問題與目的 4 第貳章 文獻回顧 5 第一節 科技接受度模型 8 第二節 機器翻譯 13 第三節 專業筆譯譯者的機器翻譯使用態度與因素 16 第四節 翻譯研究所學生的機器翻譯使用態度與因素 26 第五節 翻譯研究所學生使用機器翻譯之意圖與接受度—科技接受度模型 30 第參章 研究方法 38 第一節 研究問題 38 第二節 研究架構與研究假設 38 第三節 研究對象 41 第四節 研究工具 41 第五節 前導測試(pilot test) 42 第六節 半結構式個別訪談 43 第肆章 研究結果與討論 46 第一節 信效度分析 46 第二節 研究受訪者個人基本背景描述性統計分析 47 第三節 不同基本背景受訪者的機器翻譯接受度差異分析 56 第四節 外部變項與科技接受度模式的相關性分析 88 第五節 外部變項與科技接受度模型的多元迴歸分析 93 第六節 翻譯研究所學生與其他類型使用者機器翻譯接受度比較 117 第伍章 結論 120 第一節 研究問題與研究發現 120 第二節 研究貢獻 124 第三節 研究限制與未來研究建議 128 參考文獻 130 附錄一 過去五年 (2016-2021) 全臺研究所層級翻譯系所翻譯科技相關課程開設概況 138 附錄二 機器翻譯科技接受度問卷 140 附錄三 前導測試受訪者回饋與問卷題目修訂 148 附錄四 訪談參與同意書 153

    史宗玲(2008)。〈機器翻譯之生產及消費─由解構主義觀點論析〉。《編譯論叢》,1(1), 113-140。
    李開復、王詠剛(2017)。《人工智慧來了》(第一版)。天下文化。
    林靖文(2011)。《運用科技準備度與科技接受模型探討公共圖書館使用者使用數位服務科技之意願-以國立臺中圖書館為例》(碩士論文)。國立臺灣大學。
    林慶隆、廖柏森、張嘉倩、史宗玲、陳碧珠、陳鵬文、金超群(2021)。〈翻譯教育如何面臨AI的挑戰及如何運用AI〉。《編譯論叢》,14(1),157-181。
    金瑄桓(2020)。《臺灣口筆譯者人格類型之初探》(碩士論文)。國立臺灣師範大學。
    柯佳英、黃勇仁、張興亞(2012)。《從科技準備度探討使用者接受度之影響:以行動裝置服務為例》(碩士論文)。樹德科技大學。
    陳子瑋、林慶隆、何承恩(2012)。臺灣翻譯產學關聯研究。國家教育研究院研究報告(編號:NAER-101-24-F-2-05-00-2-03),未出版。
    陳子瑋、林慶隆、彭致翎、林俊宏、何承恩(2017)。〈臺灣大專校院翻譯課程師資及教師教學目標之研究〉。《編譯論叢》,10(1),83-120。
    陳信木、翁志遠、陳雅琪(2017)。《白話統計學》(二版)相,雙葉書廊。
    溫福星(2013)。〈社會科學研究中使用迴歸分析的五個重要觀念〉。《管理學報》,30(2),169-190。
    賴宜弘、黃芬芬、楊雪華(2015)。〈科技接受模式中文版量表之編製與相關研究〉。《亞東學報》,35,201-221。
    Al-Maroof, R. S., Salloum, S. A., AlHamadand, A. Q. M., & Shaalan, K. (2020). Understanding an Extension Technology Acceptance Model of Google Translation: A Multi-Cultural Study in United Arab Emirates. International Journal of Interactive Mobile Technologies, 3.
    Alotaibi, H. M. (2014). Teaching CAT Tools to Translation Students: an Examination of Their Expectations and Attitudes. Arab World English Journal.
    Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
    Bowker, L. (2002). Computer-aided translation technology: A practical introduction. University of Ottawa Press.
    Bowker, L. (2020). Machine translation literacy instruction for international business students and business English instructors. Journal of Business & Finance Librarianship, 25(1-2), 25-43.
    Bowker, L., & Marshman, E. (2009). Better integration for better preparation: Bringing terminology and technology more fully into translator training using the CERTT approach. International Journal of Theoretical and Applied Issues in Specialized Communication, 15(1), 60-87.
    Cadwell, P., Castilho, S., O’Brien, S., & Mitchell, L. (2016). Human factors in machine translation and post-editing among institutional translators. Translation Spaces, 5(2), 222-243.
    Cadwell, P., O’Brien, S., & Teixeira, C. S. C. (2018). Resistance and accommodation: factors for the (non-) adoption of machine translation among professional translators. Perspectives, 26(3), 301-321.
    Chan, S. (2004). Understanding internet banking adoption and use behavior: A Hong Kong perspective. Journal of Global Information Management (JGIM), 12(3), 21-43.
    Chen, H.-R., & Tseng, H.-F. (2012). Factors that influence acceptance of web-based e-learning systems for the in-service education of junior high school teachers in Taiwan. Evaluation and program planning, 35(3), 398-406.
    Chin, W. W. (1998). Commentary: Issues and opinion on structural equation modeling. MIS quarterly, vii-xvi.
    Da Silva, I. L., Alves, F., Schmaltz, M., Pagano, A., Wong, D., Chao, L., & da Silva, G. E. (2017). Translation, post-editing and directionality. Translation in Transition: Between cognition, computing and technology, 133, 107.
    Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results [Doctoral dissertation]. Massachusetts Institute of Technology.
    Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
    DePalma, D. A., Pielmeier, H., Khattou, O. (2021, July 6). Global Market Study 2021. Common Sense Advisory. https://csa-research.com/Featured-Content/For-LSPs/Global-Market-Study-2021
    DePalma, D. A., Pielmeier, H., Stewart, R. G. (2018, June 14). The Language Services Market: 2018. Common Sense Advisory. https://insights.csa-research.com/reportaction/48585/Marketing
    DePalma, D. A., Pielmeier, H., Stewart, R. G., Henderson, S. (2016, June 30). The Language Services Market: 2016. Common Sense Advisory. https://insights.csa- research.com/reportaction/36540/Marketing
    DePalma, D. A., Pielmeier, H., Stewart, R. G., Hegde, V. (2013, May 31). The Language Services Market: 2013. Common Sense Advisory. https://insights.csa-research.com/reportaction/5503/Marketing
    Doherty, G., Karamanis, N., & Luz, S. (2012). Collaboration in translation: The impact of increased reach on cross-organisational work. Computer Supported Cooperative Work (CSCW), 21(6), 525-554.
    Dragsted, B. (2004). Segmentation in translation and translation memory systems: An empirical investigation of cognitive segmentation and effects of integrating a TM system into the translation process. Samfundslitteratur.
    European Union of Associations of Translation Companies (2020). 2020 European Language Industry Survey. https://ec.europa.eu/info/sites/info/files/2020_language_industry_survey_report.pdf
    Farahat, T. (2012). Applying the technology acceptance model to online learning in the Egyptian universities. Procedia-Social and Behavioral Sciences, 64, 95-104.
    Folaron, D. (2010). Translation tools. Handbook of translation studies, 1, 429-436.
    Fields, G. S. (2003). Accounting for income inequality and its change: A new method, with application to the distribution of earnings in the United States. Worker well-being and public policy.
    Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading.
    Frérot, C. (2013, August). Incorporating translation technology in the classroom. In Way, C., Vandepitte, S., Meylaerts, R. (Eds). Tracks and Treks in Translation Studies: Selected papers from the EST Congress, Leuven 2010 (Vol. 108, p. 143). John Benjamins Publishing.
    Gaspari, F. (2001). Teaching machine translation to trainee translators: A survey of their knowledge and opinions. MT Summit VIII Workshop on Teaching Machine Translation. Santiago de Compostela, Spain. http://citeseerx.ist.psu.edu/viewdoc/
    download?doi=10.1.1.11.1147&rep=rep1&type=pdf
    Ghazizadeh, M., Lee, J. D., & Boyle, L. N. (2012). Extending the Technology Acceptance Model to assess automation. Cognition, Technology & Work, 14(1), 39-49.
    Gong, M., Xu, Y., & Yu, Y. (2004). An enhanced technology acceptance model for web-based learning. Journal of Information Systems Education, 15(4).
    Intento (2020). The State of Machine Translation 2020: Independent Multi-Domain Evaluation of Machine Translation Engines. Intento. https://try.inten.to/mt_report_2020
    Junczys-Dowmunt, M., Dwojak, T., & Hoang, H. (2016). Is neural machine translation ready for deployment? A case study on 30 translation directions. arXiv preprint. arXiv:1610.01108.
    Karamanis, N., Luz, S., & Doherty, G. (2011). Translation practice in the workplace: contextual analysis and implications for machine translation. Machine Translation, 25(1), 35-52.
    Kelly, N., DePalma, D. A., Stewart, R. G. (2012). The LanguageServices Market: 2012. Common Sense Advisory. https://www.academia.edu/7165617/CSA_Language_
    Services_Market_2012_full_text
    Koskinen, K., & Ruokonen, M. (2017). Love letters or hate mail? Translators’ technology acceptance in the light of their emotional narratives. In Kenny, D. (Ed.), Human issues in translation technology (pp. 26-42). Routledge.
    Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for information systems, 12(1), 50.
    Leong, L. W., Ibrahim, O., Dalvi-Esfahani, M., Shahbazi, H., & Nilashi, M. (2018). The moderating effect of experience on the intention to adopt mobile social network sites for pedagogical purposes: An extension of the technology acceptance model. Education and Information Technologies, 23(6), 2477-2498.
    Liébana-Cabanillas, F., Muñoz-Leiva, F., Sánchez-Fernández, J., & Viedma-del Jesús, M. I. (2016). The moderating effect of user experience on satisfaction with electronic banking: empirical evidence from the Spanish case. Information Systems and e-Business Management, 14(1), 141-165.
    Man, D., Mo, A., Chau, M. H., O’Toole, J. M., & Lee, C. (2019). Translation technology adoption: evidence from a postgraduate programme for student translators in China. Perspectives, 28(2), 253-270.
    Mellinger, C. D. (2017). Translators and machine translation: knowledge and skills gaps in translator pedagogy. The Interpreter and Translator Trainer, 11(4), 280-293.
    Nasri, W., & Charfeddine, L. (2012). Factors affecting the adoption of Internet banking in Tunisia: An integration theory of acceptance model and theory of planned behavior. The journal of high technology management research, 23(1), 1-14.
    Niño, A. (2009). Machine translation in foreign language learning: Language learners' and tutors' perceptions of its advantages and disadvantages. ReCALL, 21(2), 241-258.
    O’Brien, S. (2012). Translation as human–computer interaction. Translation Spaces, 1(1), 101-122.
    O'Hagan, M. (2019). The Routledge handbook of translation and technology. Routledge.
    Olohan, M. (2011). Translators and translation technology: The dance of agency. Translation Studies, 4(3), 342-357.
    Park, N., Lee, K. M., & Cheong, P. H. (2008). University instructors’ acceptance of electronic courseware: An application of the technology acceptance model. Journal of computer-mediated communication, 13(1), 163-186.
    Peterson, R. A. (1994). A meta-analysis of Cronbach's coefficient alpha. Journal of consumer research, 21(2), 381-391.
    Pym, A. (2011). What technology does to translating. Translation & Interpreting, 3(1), 1.
    Pym, A. (2006). Globalization and the politics of translation studies. Meta: Translators' Journal, 51(4), 744-757.
    Rossetti, A., & Gaspari, F. (2017). Modelling the analysis of translation memory use and post-editing of raw machine translation output: A pilot study of trainee translators’ perceptions of difficulty and time effectiveness. Empirical modelling of translation and interpreting, 7, 41.
    Rossi, C. (2017). Introducing statistical machine translation in translator training: from uses and perceptions to course design, and back again. Revista Tradumàtica: tecnologies de la traducció, 15, 48.
    Rossi, C., & Chevrot, J. P. (2019). Uses and perceptions of machine translation at the European Commission. The Journal of specialised translation (JoSTrans).
    Rupp, M. A., Michaelis, J. R., McConnell, D. S., & Smither, J. A. (2018). The role of individual differences on perceptions of wearable fitness device trust, usability, and motivational impact. Applied ergonomics, 70, 77-87.
    Schwab, K. (2017). The fourth industrial revolution. Currency.
    Slator (2021). Slator 2021 Language Industry Market Report. https://slator.com/slator-2021-language-industry-market-report/
    Slocum, J. (1985). A survey of machine translation: Its history, current status and future prospects. Computational linguistics, 11(1), 1-17.
    Stasimioti, M., & Sosoni, V. (2019). Undergraduate Translation Students’ Performance and Attitude vis-àvis Machine Translation and Post-editing: Does Training Play a Role. The Translating and the Computer Conference, London, U.K. https://www.asling.org/tc41/wp-content/uploads/TC41-Proceedings_125-136.pdf
    Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia manufacturing, 22, 960-967.
    Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315.
    Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
    Wu, C. S., Cheng, F. F., Yen, D. C., & Huang, Y. W. (2011). User acceptance of wireless technology in organizations: A comparison of alternative models. Computer Standards & Interfaces, 33(1), 50-58.
    Yang, Y., & Wang, X. (2019). Modeling the intention to use machine translation for student translators: An extension of Technology Acceptance Model. Computers & Education, 133, 116-126.
    Yu, J., Zo, H., Choi, M. K., & Ciganek, A. P. (2013). User acceptance of location-based social networking services: an extended perspective of perceived value. Online Information Review, 37(5), 711-730

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