Corpus approaches to news translation: We can do better than comparable!

Abstract This contribution addresses the challenging issue of building corpus resources for the study of news translation, a domain in which the coexistence of radical rewriting and close translation makes the use of established corpus-assisted analytical techniques problematic. In an attempt to address these challenges, we illustrate and test two related methods for identifying translated segments within trilingual (Spanish, French and English) sets of dispatches issued by the global news agency Agence France-Press. One relies on machine translation and semantic similarity scores, the other on multilingual sentence embeddings. To evaluate these methods, we apply them to a benchmark dataset of translations from the same domain and perform manual evaluation of the dataset under study. We finally leverage the cross-linguistic equivalences thus identified to build a ‘comparallel’ corpus, which combines the parallel and comparable corpus architectures, highlighting its affordances and limitations for the study of news translation. We conclude by discussing the theoretical and methodological implications of our findings both for the study of news translation and more generally for the study of contemporary, novel forms of translation.