Abstract
How the source text is rendered in the target language reflects a translator’s linguistics choices, which is informative of the translator’s style. Leveraging computational techniques, the present research seeks to explore translator style through word alignments derived from an entire corpus. The text material is two Chinese translations of Virginia Woolf’s novel, Jacob’s Room, by translators Pu Long and Wang Jiaxiang. Using a Transformer-based model, alignments are automatically extracted from parallel texts. A Support Vector Machine classifier is trained to test whether the alignments are indicative of the translators’ styles. Chi-square feature selection is then performed to identify the most distinguishing alignments for closer examination. Results indicate that Wang favours more explicit and literal translations, while Pu utilizes more concise, diverse, and idiomatic expressions. In addition, Wang’s translation is closer to the original, while Pu’s is more distant. This method enables us to provide a wide range of qualitative and quantitative evidence, and also observe differences not readily apparent when examining the target text alone.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call