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| 論文名稱 | Facilitating Interpreting Training through Artificial Intelligence: A Methodological Approach |
| 研討會開始日期 | 2024-04-05 |
| 研討會結束日期 | 2024-04-07 |
| 所有作者 | 林虹秀 |
| 作者順序 | 第一作者 |
| 通訊作者 | 是 |
| 研討會名稱 | ATISA XI Conference |
| 是否具有對外公開徵稿及審稿制度 | 是 |
| 研討會舉行之國家 | NATUSA-美國 |
| 研討會舉行之城市 | New Jersy |
| 發表年份 | 2024 |
| 所屬計劃案 | 無 |
| 可公開文檔 |
|
| 可公開文檔 | |
| 可公開文檔 |
[摘要] :
Facilitating Interpreting Training through Artificial Intelligence: A Methodological Approach
Eileen Hung-Hsiu Lin
Assistant Professor
Department of Translation and Interpreting
Wenzao Ursuline University of Languages
Kaohsiung, Taiwan
Abstract
Recent advancements in the field of interpreter training have seen the integration of Artificial Intelligence (AI), particularly generative large-scale language models (LLMs) like ChatGPT, in Taiwan. This paper examines the integration of AI in postgraduate interpreting programs in Taiwan, emphasizing the pedagogical implications for Chinese A/English B students. The core challenges in these programs revolve around enhancing English listening and speaking competencies. AI technologies, including ChatGPT, Paraphraser Online, and Google’s Speech-to-Text, are being leveraged to facilitate understanding, refine language, and enable swift cross-linguistic translation. The current study employs a mixed-methods approach, intertwining qualitative interviews with student interpreters and a quantitative analysis of performance metrics. This approach aims to discern the impact of AI on the training and proficiency of interpreters, especially regarding grammar correction, real-time feedback, and students’ learning motivation and satisfaction with AI. Preliminary findings suggest that while AI tools substantially augment the repository of speech materials and linguistic support available to trainees, they exhibit limitations in capturing language's cultural and emotional nuances. Hence, a hybridized pedagogical model that integrates the efficiency of AI with the indispensable human capacity for intuition and cultural insight is recommended in this paper. The study's 15-student cohort underwent a comparative analysis that juxtaposed original interpretations, AI-assisted interpretations, and self-assessments. The results support an instructional paradigm combining AI's capabilities with human teachers' nuanced feedback. As the demand for interpreters persists, despite burgeoning AI proficiencies, the findings reinforce the argument that the efficacy of interpreting outcomes hinges on active human mediation. The study emphasizes the need for a balanced approach to interpreter training, combining Artificial Intelligence (AI) benefits with human expertise to produce interpreters ready for the digital age's complex challenges.
Keywords: interpreter training, generative AI, Large Language Models (LLM), pedagogical implications, Hybrid Instructional Model.
[英文摘要] :
Facilitating Interpreting Training through Artificial Intelligence: A Methodological Approach
Eileen Hung-Hsiu Lin
Assistant Professor
Department of Translation and Interpreting
Wenzao Ursuline University of Languages
Kaohsiung, Taiwan
Abstract
Recent advancements in the field of interpreter training have seen the integration of Artificial Intelligence (AI), particularly generative large-scale language models (LLMs) like ChatGPT, in Taiwan. This paper examines the integration of AI in postgraduate interpreting programs in Taiwan, emphasizing the pedagogical implications for Chinese A/English B students. The core challenges in these programs revolve around enhancing English listening and speaking competencies. AI technologies, including ChatGPT, Paraphraser Online, and Google’s Speech-to-Text, are being leveraged to facilitate understanding, refine language, and enable swift cross-linguistic translation. The current study employs a mixed-methods approach, intertwining qualitative interviews with student interpreters and a quantitative analysis of performance metrics. This approach aims to discern the impact of AI on the training and proficiency of interpreters, especially regarding grammar correction, real-time feedback, and students’ learning motivation and satisfaction with AI. Preliminary findings suggest that while AI tools substantially augment the repository of speech materials and linguistic support available to trainees, they exhibit limitations in capturing language's cultural and emotional nuances. Hence, a hybridized pedagogical model that integrates the efficiency of AI with the indispensable human capacity for intuition and cultural insight is recommended in this paper. The study's 15-student cohort underwent a comparative analysis that juxtaposed original interpretations, AI-assisted interpretations, and self-assessments. The results support an instructional paradigm combining AI's capabilities with human teachers' nuanced feedback. As the demand for interpreters persists, despite burgeoning AI proficiencies, the findings reinforce the argument that the efficacy of interpreting outcomes hinges on active human mediation. The study emphasizes the need for a balanced approach to interpreter training, combining Artificial Intelligence (AI) benefits with human expertise to produce interpreters ready for the digital age's complex challenges.
Keywords: interpreter training, generative AI, Large Language Models (LLM), pedagogical implications, Hybrid Instructional Model.
[參考文獻] :
References
N. Li, "Study of Interpretation Training on the Development of Foreign-Related Pharmaceutical Enterprises from the Perspective of Artificial Intelligence —Taking Rising of Guangdong-Hong Kong-Macao Greater Bay Area," 2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC), Hangzhou, China, 2022, pp. 265-268, doi: 10.1109/ICNISC57059.2022.00060.
Wu, Yunyun & Wang, Yu. (2018). An Exploration of English-Chinese AI Interpreting Difficulties from a Perspective of Practical Application. DEStech Transactions on Social Science, Education and Human Science. 10.12783/dtssehs/ichae2018/25626.
Gloria Corpas Pastor & Bart Defranq (Eds.). (2023). Interpreting Technologies – Current and Future Trends. John Benjamins. https://doi.org/10.24197/her.25.2023.519-525
Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at Work. NBER Working Paper Series.
