Automatic MT Error Analysis: Hjerson Helping Addicter

Jan Berka; Ondrej Bojar; Mark Fishel; Maja Popovic; Daniel Zeman

In: Proceedings of the 8th International Conference on Language Resources and Evaluation. International Conference on Language Resources and Evaluation (LREC-12), 8th, May 23-25, Istanbul, Turkey, European Language Resources Association (ELRA), 5/2012.


We present a complex, open source tool for detailed machine translation error analysis providing the user with automatic error detection and classification, several monolingual alignment algorithms as well as with training and test corpus browsing. The tool is the result of a merge of automatic error detection and classification of Hjerson (Popović, 2011) and Addicter (Zeman et al., 2011) into the pipeline and web visualization of Addicter. It classifies errors into categories similar to those of Vilar et al. (2006), such as: morphological, reordering, missing words, extra words and lexical errors. The graphical user interface shows alignments in both training corpus and test data; the different classes of errors are colored. Also, the summary of errors can be displayed to provide an overall view of the MT system's weaknesses. The tool was developed in Linux, but it was tested on Windows too.


hjerson+addicter.pdf (pdf, 613 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence