研究生: |
劉子瑞 Liu, Zi-Rui |
---|---|
論文名稱: |
學生口譯員對 ChatGPT 輔助遠距英進中逐步口譯之感知 Student Interpreters’ Perception of ChatGPT-assisted Remote English-Chinese Consecutive Interpreting |
指導教授: |
汝明麗
Ju, Ming-Li |
口試委員: |
汝明麗
Ju, Ming-Li 陳子瑋 Chen, Tze-Wei 張嘉倩 Chang, Chia-Chien |
口試日期: | 2023/07/24 |
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: | 2024 |
畢業學年度: | 112 |
語文別: | 英文 |
論文頁數: | 100 |
中文關鍵詞: | 遠距口譯 、電腦輔助逐步口譯 、ChatGPT 、學生口譯員 、感知 |
英文關鍵詞: | Remote interpreting, CACI, ChatGPT, Student interpreter, Perception |
研究方法: | 實驗設計法 |
DOI URL: | http://doi.org/10.6345/NTNU202400551 |
論文種類: | 學術論文 |
相關次數: | 點閱:111 下載:6 |
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本實徵研究旨在探討學生口譯員對 ChatGPT 輔助遠距英進中逐步口譯的感知。三十三位實驗參與者來自全臺六所翻譯所,遠端在 Google Meet 上觀看模擬影片並進行逐步口譯。影片模擬結合自動語音辨識(Voice Control for ChatGPT)和機器翻譯(ChatGPT)所進行的逐步口譯。實驗結合問卷和訪談,量性和質性分析可從幾個面向來看。首先,參與者對於六項使用者經驗分數有正面感知,即,認知易用、認知有用、介面友善、工作流暢度、壓力,以及足夠時間使用科技工具。然而,使用者經驗分數與修課時數之間無統計相關性,綜合訪談內容,這意味著在該模式下所需的口譯能力可能與傳統模式不同。此外,參與者對 ChatGPT 翻譯的修改內容,也反映出對翻譯品質的感知。在後續的半結構式訪談中,受訪者分享對於該口譯模式的想法,優點像是抓得到重要細節、節省氣力,缺點則涵蓋分心、版面呈現、翻譯品質、過度依賴和自我懷疑;不同階段使用的口譯策略,也有助於理解認知過程;而關於可能性和普及性、課程訓練建議,以及個人因應的訪談內容,也進一步豐富了實務意涵。
The empirical research aims to investigate how student interpreters perceive the mode of ChatGPT-assisted remote English-to-Chinese consecutive interpreting. The experiment, conducted via Google Meet, involved 33 participants performing consecutive interpreting (CI) with a simulation video. The video demonstrated the application of automatic speech recognition and machine translation. The experiment combined a questionnaire and a semi-structured interview and the analyses revealed several aspects of student interpreters’ perceptions. First, participants showed positive perception in six user experience scores, namely perceived ease of use, perceived usefulness, interface friendliness, workflow, stress, and time for technology tools. However, no correlations were found between user experience scores and class hours. This quantitative analysis, taken together with a qualitative analysis of the interviewees’ responses suggests that the interpreting abilities required in ChatGPT-assisted CI might be different from conventional CI. Additionally, participants’ modifications to ChatGPT-generated Chinese translations reflected their perceptions of the translation quality.
In the semi-structured interview, participants offered valuable feedback about the interpreting mode. They highlighted advantages such as capturing critical details and saving effort, while noting disadvantages associated with distraction, display format, translation quality, overreliance, and self-doubt. In addition, interviewees’ interpreting strategies also shed light on the cognitive process in the interpreting mode. The interviews also discussed the potential for adoption and popularity, suggestions for courses and training, and individual responses, which enrich the practical implications of the research. In conclusion, the results of student interpreters’ perceptions of the mode and the subsequent practical implications not only enhance our understanding of the perception of computer-assisted consecutive interpreting (CACI) but serve to identify the strengths and limitations of specific computer-assisted interpreting (CAI) tools.
AIIC. (2015, January 18). AIIC and distance interpreting. Retrieved March 3, 2023, from https://aiic.org/site/world/about/profession/distanceinterpreting
Barnard, D. (2022, November 8). Average speaking rate and words per minute. Retrieved February 26, 2023, from https://virtualspeech.com/blog/average-speaking-rate-words-per-minute
BasuMallick, C. (2022, April 14). Top 10 speech recognition software and platforms in 2022. Spiceworks. Retrieved June 26, 2023, from https://www.spiceworks.com/tech/artificial-intelligence/articles/speech-recognition-software/
Braun, S. (2019). Technology and interpreting. In M. O’Hagan (Ed.), The Routledge handbook of translation and technology (pp. 271–288). Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9781315311258-19/technology-interpreting-sabine-braun
Buján, M., & Collard, C. (2023). Remote simultaneous interpreting and COVID-19: Conference interpreters’ perspective [EBook]. In K. Liu & A. Cheung (Eds.), Translation and Interpreting in the Age of COVID-19 (Vol. 9, pp. 133–150). Springer. https://doi.org/10.1007/978-981-19-6680-4_7
Chen, S., & Kruger, J. (2022). The effectiveness of computer-assisted interpreting : A preliminary study based on English-Chinese consecutive interpreting. Translation and Interpreting Studies, 17(3). https://doi.org/10.1075/tis.21036.che
Connelly, L. M. (2008). Pilot studies. Medsurg Nursing, 17(6), 411–412.
Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Defrancq, B., & Fantinuoli, C. (2021). Automatic speech recognition in the booth. Target, 33(1), 73–102. https://doi.org/10.1075/target.19166.def
Densmer, L. (2020, April 15). Computer-aided interpretation: The latest tools for interpreters. RWS Blog. Retrieved February 26, 2023, from https://www.rws.com/blog/computer-aided-interpretation-the-latest-tools-for-interpreters/
Desmet, B., Vandierendonck, M., & Defrancq, B. (2018). Simultaneous interpretation of numbers and the impact of technological support [Ebook]. In C. Fantinuoli (Ed.), Interpreting and technology (pp. 13–28). Language Science Press. https://doi.org/10.5281/zenodo.1493281
Diur, M., & Ruiz-Rosendo, L. (2023). Reconceptualising interpreting at the United Nations [EBook]. In K. Liu & A. Cheung (Eds.), Translation and Interpreting in the Age of COVID-19 (Vol. 9, pp. 151–163). Springer. https://doi.org/10.1007/978-981-19-6680-4_8
Fantinuoli, C. (2017). Speech recognition in the interpreter workstation [Paper presentation]. Translating and the Computer 39, London, United Kingdom of Great Britain and Northern Ireland. https://www.asling.org/tc39/
Fantinuoli, C. (2018a). Interpreting and technology: The upcoming technological turn. In C. Fantinuoli (Ed.), Interpreting and technology (pp. 1–12). Berlin: Language Science Press. https://doi.org/10.5281/zenodo.1493281
Fantinuoli, C. (2018b). Computer-assisted interpreting: Challenges and future perspectives. In G. C. Pastor & I. Durán-Muñoz (Eds.), Trends in E-Tools and Resources for Translators and Interpreters (pp. 153–174). Brill. https://doi.org/10.1163/9789004351790_009
Fantinuoli, C., & Montecchio, M. (2022, December 15-17). Defining maximum acceptable latency of AI-enhanced CAI tools [Conference session]. techLING2021, online. https://doi.org/10.48550/arXiv.2201.02792
Fantinuoli, C., & Prandi, B. (2021). Towards the evaluation of automatic simultaneous speech translation from a communicative perspective. Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.iwslt-1.29
Frąckiewicz, M. (2023, June 14). The impact of ChatGPT on translation and interpretation. TS2 SPACE. Retrieved June 23, 2023, from https://ts2.space/en/the-impact-of-chatgpt-on-translation-and-interpretation/
Gile, D. (2009). Basic concepts and models for interpreter and translator training (Revised). John Benjamins. https://doi.org/10.1075/btl.8
Gile, D. (2021). The effort models of interpreting as a didactic construct. In R. Muñoz-Martín, S. Sun, & D. Li (Eds.), Advances in cognitive translation studies (pp. 139–160). Springer. https://doi.org/10.1007/978-981-16-2070-6_7
Goldsmith, J. (2018). Tablet interpreting. Translation and Interpreting Studies, 13(3), 342–365. https://doi.org/10.1075/tis.00020.gol
Guo, M., Han, L., & Anacleto, M. T. (2022). Computer-assisted interpreting tools: Status quo and future trends. Theory and Practice in Language Studies, 13(1), 89–99. https://doi.org/10.17507/tpls.1301.11
Hale, S., & Napier, J. (2013). Research methods in interpreting: A practical resource. Bloomsbury Publishing. https://www.bloomsbury.com/uk/research-methods-in-interpreting-9781472524737/
Hughes, A. (2023, June 30). ChatGPT: Everything you need to know about OpenAI’s GPT-4 tool. Science Focus. Retrieved July 4, 2023, from https://www.sciencefocus.com/future-technology/gpt-3/
Jiao, W., Wang, W., Huang, J.-T., Wang, X., & Tu, Z. (2023). Is ChatGPT a good translator? Yes with GPT-4 as the engine. Tencent AI Lab. Retrieved June 9, 2023, from https://doi.org/10.48550/arXiv.2301.08745
Lardinois, F. (2020, April 29). Google is making Meet free for everyone. TechCrunch. Retrieved June 26, 2023, from https://techcrunch.com/2020/04/29/google-is-making-meet-free-for-everyone/?guccounter=1&guce_referrer=aHR0cHM6Ly9lbi53aWtpcGVkaWEub3JnLw&guce_referrer_sig=AQAAAJpBioYewzKryFlgsUf4x4vezfeZDTyz0KlnUyKSTeQq1LfYQ6S68sN-Auqq57Y4OyevtaXiqGPRnZIHR85P4PwSITzMVnFQVgnFZqTP3wuBTzcuzFs5ftKmu0GDoDE2myapOhTrtTVpCHqoeDeezbSnp2N3vX5VoJRJTvBimzMU
Lewis, J. (2019). Measuring user experience with 3, 5, 7, or 11 points: Does it matter? Human Factors, 63(6), 999–1011. https://doi.org/10.1177/0018720819881312
Liu, K., & Cheung, A. (2022). Translation and interpreting in the age of COVID-19: Challenges and opportunities. In K. Liu & A. Cheung (Eds.), Translation and Interpreting in the Age of COVID-19 (pp. 1–10). Springer. https://doi.org/10.1007/978-981-19-6680-4_1
Mellinger, C. (2019). Computer-Assisted interpreting technologies and interpreter cognition: A product and Process-Oriented perspective. Revista Tradumàtica, 17, 33–44. https://doi.org/10.5565/rev/tradumatica.228
Mellinger, C. D. (2022). Cognitive behavior during consecutive interpreting: Describing the notetaking process. Translation & Interpreting, 14(2), 103–119. https://doi.org/10.12807/ti.114202.2022.a07
Ortiz, L. A., & Cavallo, P. (2018). Computer-assisted interpreting tools (CAI) and options for automation with automatic speech recognition. TradTerm, 32, 9–31. https://doi.org/10.11606/issn.2317-9511.v32i0p9-31
Pastor, G. C. (2018). Tools for interpreters: The challenges that lie ahead. Current Trends in Translation Teaching and Learning E, 5, 138–182. https://doi.org/10.5281/zenodo.5940648
Pastor, G. C. (2022). Technology solutions for interpreters: The VIP system. Hermeneus, 23, 91–123. https://doi.org/10.24197/her.23.2021.91-123
Pastor, G. C., & Fern, L. M. (2016). A survey of interpreters’ needs and practice related to language technology (No. FFI2012-38881-MINECO/TI-DT-2016–1). University of Malaga.
Pisani, E., & Fantinuoli, C. (2021). Measuring the impact of automatic speech recognition on number rendition in simultaneous interpreting. In C. Wang & B. Zheng (Eds.), Empirical Studies of Translation and Interpreting (pp. 181–197). Routledge. https://doi.org/10.4324/9781003017400-14
Prandi, B. (2017, November 17). Designing a multimethod study on the use of CAI tools during simultaneous interpreting [Conference session]. Translating and the Computer 39, London. https://www.asling.org/tc39/
Prandi, B. (2018). An exploratory study on CAI tools in simultaneous interpreting: Theoretical framework and stimulus validation [Ebook]. In C. Fantinuoli (Ed.), Interpreting and technology (pp. 29–59). Language Science Press. https://doi.org/10.5281/zenodo.1493293
Prandi, B. (2020). The use of CAI tools in interpreter training: Where are we now and where do we go from here? InTRAlinea. https://www.intralinea.org/specials/article/2512
Prandi, B. (2023). Computer-assisted simultaneous interpreting: A cognitive-experimental study on terminology. Language Science Press. https://doi.org/10.5281/zenodo.7143055
Ribas, M. A. (2013). Problems and strategies in consecutive interpreting: A pilot study at two different stages of interpreter training. Meta, 57(3), 812–835. https://doi.org/10.7202/1017092ar
Rondan-Cataluña, F. J., Arenas-Gaitán, J., & Ramírez-Correa, P. E. (2015). A comparison of the different versions of popular technology acceptance models: A non-linear perspective. Kybernetes, 44(5), 788–805. https://doi.org/10.1108/k-09-2014-0184
Sabzalieva, E., & Valentini, A. (2023). ChatGPT and artificial intelligence in higher education: Quick start guide. In The UNESCO Digital Library. The United Nations Educational, Scientific and Cultural Organization. Retrieved June 8, 2023, from https://unesdoc.unesco.org/ark:/48223/pf0000385146.locale=en
Sauro, J. (2019, May 8). 10 things to know about the technology acceptance model. MeasuringU. Retrieved June 26, 2023, from https://measuringu.com/tam/
Seeber, K., Keller, L., Amos, R., & Hengl, S. (2019). Expectations vs. Experience: Attitudes towards video remote conference interpreting. Interpreting, 21(2), 270–304. https://doi.org/10.1075/intp.00030.see
Setton, R., & Dawrant, A. (2016). Conference interpreting. A complete course. John Benjamins. https://doi.org/10.1075/btl.120
Vlad, M. (2022, December 19). How will ChatGPT impact the translation industry? RWS Blog. Retrieved June 23, 2023, from https://www.rws.com/blog/chatgpt-impact-translation-industry/
Wan, H., & Yuan, X. (2022). Perceptions of CAI tools in English/Chinese interpreting practice, perspectives of professional interpreters and trainers. Transletters, 6, 151–179. https://journals.uco.es/tl/article/view/13656
Wang, W., & Li, D. (2015). How student and professional interpreters in Chinese-English consecutive interpreting differ in their choice of interpreting strategies. Chinese Translators Journal, 6, 41–47. https://doi.org/10.13140/RG.2.2.36782.05447
Wang, X., & Wang, C. (2019). Can computer-assisted interpreting tools assist interpreting? Transletters, 3, 109–139. https://www.uco.es/ucopress/ojs/index.php/tl/article/view/11575
Xu, R. (2018). Corpus-based terminological preparation for simultaneous interpreting. Interpreting, 20(1), 29–58. https://doi.org/10.1075/intp.00002.xu
Zhao, N. (2022). Use of Computer-Assisted interpreting tools in conference interpreting training and practice during COVID-19. In K. Liu & A. Cheung (Eds.), Translation and Interpreting in the Age of COVID-19 (pp. 331–347). Springer. https://doi.org/10.1007/978-981-19-6680-4_17