Demand for Chinese-to-English translation has increased over recent years. In contrast, resources for training translators for Chinese-to-English are few although increasing now, relative to English-to-Chinese for example. Corpus-based techniques are now more widely acknowledged as being appropriate for the study of translation. A number of Chinese/English parallel translation corpora have been built and applied to the research of translation practice. While such corpus
resources have made a significant impact on these research areas, they suffer from problems due to the skewed nature of translated text, or ‘translationese’. Obviously, translators and translation systems trained on these parallel corpora would inevitably inherit these features. Comparable corpora such as news articles, science and technology reports from the same period are more readily available. Studying translation revision carried out by native speakers of English may offer one way in to study Chinese-to-English translationese. However, very few quantitative studies of the products of the translation revision process have been carried out for any language pair. In this paper, we develop a framework using techniques from corpus linguistics, to enable the quantitative study of the translation revision process and describe the initial results we obtained.
The research fits within a wider project to train language models in software tools that will assist in searching for non-native features of translated English texts.