Poor mans lemmatisation for automatic error classification

This paper demonstrates the possibility to make an existing automatic error classifier for machine translations independent from the requirement of lemmatisation. This makes it usable also for smaller and under-resourced languages and in situations where there is no lemmatiser at hand. It is shown that cutting all words into the first four letters is the best method even for highly inflective languages, preserving both the detected distribution of error types within a translation output as well as over various translation outputs. The main cost of not using a lemmatiser is the lower accuracy of detecting the inflectional error class due to its confusion with mistranslations. For shorter words, actual inflectional errors will be tagged as mistranslations, for longer words the other way round. Keeping all that in mind, it is possible to use the error classifier without target language lemmatisation and to extrapolate inflectional and lexical error rates according to the average word length in the analysed text.