Translation quality Sample Clauses

Translation quality. We will compare the results of MOLTO to other translation tools, by using both automatic metrics (BLEU, Bilingual Evaluation Understudy, Xxxxxxxx & al. 2002) and, in particular, the human evaluation of “utility”, as defined by TAUS. The comparison is performed with the freely available general-purpose tools Google translate and Systran. While the comparison is “unfair” in the sense that MOLTO is working with special-purpose domain grammars, we want to perform measurements that confirm that MOLTO’s quality really is essentially better. Comparisons with domain-specific systems will be performed as well, if any such systems can be found. Domain-specific translation systems are still rare and/or not publicly available. Regarding automatic metrics for MT, the usage of lexical n-gram based metrics (WER, PER, BLEU, NIST, ROUGE, etc.) represents the usual practice in the last decade. However, recent studies showing some limitations of lexical metrics at capturing certain kind of linguistic improvements and making appropriate rankings of heterogeneous MT systems Xxxxxxxx-Xxxxx et al. (2006); Xxxxxxxx-Xxxxx et al. (2007); Xxxxxxxx-Xxxxx et al. (2008); Xxxxxxx (2008) have fostered research on more sophisticated metrics, which can combine several aspects of syntactic and semantic information. The IQmt suite33, developed by the UPC team, is one of the examples in this direction Giménez and Xxxxx (2006); Xxxxxxx and Xxxxxxx (2008). In IQmt, a number of automatic metrics for MT, which exploit linguistic information from morphology to semantics, are available for the English language and will be extended to other languages (e.g., Spanish) soon. These metrics are able to capture more subtle improvements in translation and show high correlation with human assessments Xxxxxxx and Xxxxxxx (2008); Xxxxxxxx-Xxxxx et al. (2008). We plan to use IQmt in the development cycle whenever it is possible. For languages not covered in IQmt, we will rely on BLEU (Xxxxxxxx et al. 2002). Regarding human evaluation, the TAUS method is the more appropriate one for the MOLTO tasks, since we are aiming for reliable rendering of information. It consists of inspection of a significant number of source/target segments to determine the effectiveness of information transfer. The evaluator first reads the target sentence, then reads the source to determine whether additional information was added or misunderstandings identified. The scoring method is as follows: