Applying data-driven learning in self-translation of academic discourse: A case study of a Chinese medical student

Abstract

This article reports on an experiment on the use of data-driven learning (DDL) in the revision of self-translation by a Chinese medical student. The think-aloud method is employed to investigate the difficulties the student encountered in self-translation and the effectiveness of DDL in improving the quality of self-translation. Results show that difficulties in the self-translation of medical abstracts are mostly associated with markers of rhetorical moves, terminologies, and conventional academic expressions and that they can be effectively solved by such corpus consultation strategies as checking possible options in bilingual 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 a positive attitude toward DDL.