Towards Recognizing Phrase Translation Processes: Experiments on English-French
Table 7: Classification results after grouping classes, every class has 549 instances
['Configuration', 'average accuracy', 'average F1 (class1)', 'average F1 (class2)']
Our error analysis shows that in binary classification, it is difficult to distinguish Literal and Equivalence; in multi-class classification, the biggest confusion is between Equivalence and Contain_Transposition. Literal combined with Equivalence (E), vs the other classes iii) Literal combined with Equivalence and Transposition (T), vs the other classes. The third configuration is more interesting, because the group of translation processes LET do not bring meaning changes, while the processes non-LET could. The results show that by including Transposition (change grammatical classes without changing the meaning), the performance gets better than only grouping Literal and Equivalence, since we avoid the confusion between Equivalence and Transposition. The better results of binary classification (L vs NL, LET vs non-LET) indicate that in future work we can develop cascading classifiers, namely first separating word-for-word literal translations, or those which do not cause meaning changes, then conducting a finer-grained classification among the other categories.