Didactic potential of working with DIY corpora and text mining approaches in literary translation training

Corpus analysis methods have been widely employed in literary translation research by numerous scholars. However, their integration into literary translation training has yet to be developed. With the advancement of AI technology, this paper explores the potential of employing AI-enhanced corpus text analysis and text mining techniques in this context. We propose a curriculum design that combines corpus and text mining methods structured in three stages. In the first stage, students in a literary translation course learn to build their own DIY parallel corpus, using distant reading tools for a comparative analysis of the linguistic and stylistic features of the source text and its translated texts. The next stage introduces text mining techniques for paratextual analysis of translations, including exploration tasks such as naming entity recognition, topic modelling, keyword extraction, text summarisation, and sentiment analysis. Based on the findings from corpus searches and text mining analyses, students develop a retranslation plan. The final stage involves the presentation of their retranslations and corresponding paratexts. The effectiveness of this course is assessed through evaluations and student feedback, highlighting the value of integrating text mining approaches in literary translation training.