Applying data-driven learning in self-translation of academic discourse: A case study of a Chinese medical student - Lyu, Ying; Han, Ziman (Frontiers in Psychology, 2023)
This paper reports an experiment on the use of data-driven learning (DDL) in revision of self-translation by a Chinese medical student. The think-aloud method is employed to investigate difficulties the student encountered in self-translation and the effectiveness of DDL in improving quality of self-translation. Results show that difficulties in self-translation of medical abstracts are mostly associated with markers of rhetorical moves, terminologies, and conventional academic expressions, and they can be effectively solved by such corpus consultation strategies as checking possible options in bi-lingual dictionaries, using the most-certain keywords to find collocations, and using the most-possible accompanying words to find contexts. A comparison of translations before and after the application of DDL reveals that it could help improve translation quality in lexical choices, syntactic structures, and discourse practice. An immediate interview shows that the participant holds positive attitudes towards DDL