2027 Summer Research Program

This research project requires 2 students, preferably current postgraduate students in the MATI program.

Duration: 6 weeks (between 11 January to 19 February 2027)
Hours of engagement: 36 hours per week
Delivery mode: Hybrid (on-site and online)
Location: School of Languages and Cultures, UQ St Lucia.

This empirical study bridges a salient gap in the existing interpreting studies literature. It aims to explore the unique features of real-life Mandarin/English simultaneous interpretations produced by highly skilled professional conference interpreters at the United Nations in comparison with AI machine interpretations instantly generated by DeepL Voice.

Simultaneous interpreting is a highly challenging cognitive and linguistic activity in which an interpreter listens to a source speech (e.g., an English speech by the prime minister of Australia) and at the same time produces the messages in the target language (e.g., Mandarin or Japanese). Simultaneous interpreting at international conferences represents the highest level of interpreting work for practitioners in the translation and interpreting profession. In contrast to the large body of research on translation, there has been little empirical research comparing real-life conference interpreting with machine interpreting. Such research is significant in that knowing how professional conference interpreters cope with interpreting difficulties in real-life simultaneous interpreting work can inform interpreting students about how to deal with challenges and solve problems during simultaneous interpreting, which is often quite stressful and difficult for students. Moreover, such research can provide evidence regarding the irreplaceable qualities of professional human interpreters’ simultaneous interpretations.

Students’ assistance with data analysis will speed up the progress of this project.

Students will undertake the following key activities throughout the project:

• Transcribe English and Mandarin audio-recordings and produce draft transcripts.
• Check the accuracy of machine-generated draft transcripts and others’ transcripts and make necessary revisions to produce accurate transcripts.
• Analyse the accuracy of Mandarin/English simultaneous interpretations.
• Analyse interpreters’ simultaneous interpreting strategies.

Note: It’s okay if students don’t have such research experiences. Students will be trained by the chief investigator Dr Lily Wang to conduct the data analyses.

Students will gain the following skills and experience students from participating in the project:

• Enhanced listening comprehension skills in both English and Mandarin,
• Ability to analyse text-based linguistic data,
• Ability to conduct qualitative analysis of data,
• Ability to conduct corpus-based research,
• Time-management skills,
• Goal-oriented work methods.

The expected outputs or achievements by the end of the project:

• Mandarin/English simultaneous interpreting audio-recordings will be organised into texts and Excel spreadsheets ready for data analysis in terms of both accuracy of interpreting performance and simultaneous interpreting strategies.
• Draft papers on simultaneous interpreting
• A better understanding of human interpreting and machine interpreting

This project is open to applications from undergraduate and postgraduate students with high proficiency in both English and Chinese. However, second-year or first-year students in the Master of Arts in Translation and Interpreting (MATI) program are particularly suitable for the project due to their relevant training.

Students will work with supervisor Dr Lily Wang, lecturer in Translation and Interpreting in the Master of Arts in Translation and Interpreting (MATI) program at School of Languages and Cultures, UQ.

Additional opportunities associated with the project include both possible ongoing research assistant work in this or related projects and potential opportunity to write publications together, depending on students’ abilities and contributions.

Students may contact the supervisor before applying.

Direct enquiries to:  Dr Lily Wang  (j.wang7@uq.edu.au)