Register-Based Machine Translation Evaluation with Text Classification Techniques

This paper presents a novel approach to machine translation evaluation by combining register features – characterised by particular distributions of lexico-grammatical features – with text classification techniques. The goal of this method is to compare machine translation output with comparable originals in the same language, as well as with human reference translations. The degree of similarity – in terms of register features – between machine translations and originals, and machine translations and reference translations is measured by applying two text classification methods trained on 1) originals and 2) reference translations, and tested on machine translations. The results from the experiments prove our assumption that machine translations share register features rather with human translations than with non-translated texts produced by humans. This confirms that registers are one of the most important factors that should be integrated into register-based machine translation evaluation.