The UN Parallel Corpus Annotated for Translation Direction
This work addresses the need for annotated data in translation studies and NLP, but it is incremental as it applies existing classification methods to a new dataset.
The paper tackled the problem of distinguishing between translated and original text in the UN protocol corpus by modeling it as a classification task, achieving up to 95% accuracy, and made the annotated parallel corpus publicly available.
This work distinguishes between translated and original text in the UN protocol corpus. By modeling the problem as classification problem, we can achieve up to 95% classification accuracy. We begin by deriving a parallel corpus for different language-pairs annotated for translation direction, and then classify the data by using various feature extraction methods. We compare the different methods as well as the ability to distinguish between translated and original texts in the different languages. The annotated corpus is publicly available.