A Hand-Held Multimedia Translation and Interpretation System with Application to Diet Management
TABLE VII: Translation accuracy, manually evaluated, for versions 1, 2 and 3 of our system, and Google Translate. Top-1 means the first translation is the correct one.
['[BOLD] Engine Google', '[BOLD] Correct 365', '[BOLD] Top-1 -', '[BOLD] Incorrect 135', '[BOLD] Accuracy 73%', '[BOLD] Top-1 -']
We tested the accuracy of all versions of our system, as well as that of Google Translate, using our test set. Note that Google Translate has even been excluded from the last Workshop on Statistical Machine Translation events because of the large margin between Google and many of the other systems. However, it is important to be aware of the limitations of Google Translate. Therefore, it is unlikely to be useful to scientific or medical contexts in the near future All the entries were extracted from different online sources, unrelated to the sources from where we built our corpora. All incorrect translations in v2 and v3 are due to non-existing words in the phrase-table, or gender and number grammatical errors. Most errors in Google Translate are literally correct but incorrect in a food-related context. For example, andrajos, which is a Spanish stew, is translated to rag. The first version (v1) has a slightly better accuracy than Google. However, v2 and v3 are both significantly more accurate. This indicates that translation standardization and n-gram consolidation does indeed increase translation accuracy.