5. Findings. Students' mt error statistics

The findings in this MT-based project will be discussed in two areas: 1) statistical MT errors and the result of questionnaire responses for a quantitative analysis; 2) a summary of students' reflections for a qualitative analysis.

In the students' first week assignments, the average number of lexical/pragmatic MT errors in the informative text type was 4 and the average number of syntactic MT errors was 1. In contrast, lexical/pragmatic MT errors in the evocative/operative text type averaged 5 and syntactic MT errors averaged 3. In students' second- and third-week assignments, the average number of the lexical/pragmatic MT errors in the informative text type was 7, and that in the evocative/operative text type was 10, and that in the expressive text type was 11. However, their average numbers of syntactic errors was 1 in the informative text type, 3.5 in the evocative/operative text type, and 1.5 in the expressive text type. The average numbers of MT errors in the students' three-week assignments along with some examples of post-MT editing are tabulated below.

We summarize students' reflections as follows. Students generally found that the average number of lexical, pragmatic and syntactic errors in the MT output of the informative text type was lower than in the evocative/operative and expressive text types. They observed that the excerpts from the drama and fiction or passages extracted from the advertisement and speech contained many metaphorical expressions that required translators to use esthetic or artistic language to modify the literal translations produced by the MT system. In the meantime, they found that most of the lexical items in the expressive text type were used to describe facts and embody referential meanings, so that these items could be more satisfactorily processed by the MT system. In addition, the rule-based computing parser of the MT system was incapable of analyzing the complicated sentence structures in advertisements and literary works. In contrast, the user's manual or product instructions tended to use simple, imperative sentence structures, so that the MT system could process these sentences more accurately. Finally, students realized that comparative analysis of MT errors in the three text types helped them to realize that each text type had its special functions and dominant linguistic features, and these linguistic factors affected the MT performance and led to different numbers of MT errors.

6. Using mt error analysis to identify text types

The result of the questionnaire shows that 85% of the students, have favorable responses to the second part of the questionnaire (Questions 1 to 5). This indicates that a majority of students agrees with the use of MT error analysis and post-MT editing to learn text types in translation. MT errors analysis facilitates students' active mental involvement because students need to make cross-references between SL and TL, and then assess what linguistic features in a text type prevent satisfactory processing by the MT system. Students' approval of the effectiveness of MT error analysis changes our assumption that MT errors mislead students and interfere with their development of translation competence. Actually, MT errors can benefit students if used in the right way.