Translation of Unseen Bigrams by Analogy Using an SVM Classifier
Hao Wang, Lu Lyu, Lu Lyu, 81645, Yves Lepage, 80187, 70573608 · Institutional Repositories DataBase (IRDB) · 2015
Detecting language divergences and predict-ing possible sub-translations is one of the most essential issues in machine translation. Since the existence of translation divergences, it is impractical to straightforward translate from source sentence into target sentence while keeping the high degree of accuracy and with-out additional information. In this paper, we investigate the problem from an emerging and special point of view: bigrams and the cor-responding translations. We first profile cor-pora and explore the constituents of bigrams in the source language. Then we translate un-seen bigrams based on proportional analogy and filter the outputs using an Support Vector Machine (SVM) classifier. The experiment re-sults also show that even a small set of features from analogous can provide meaningful infor-mation in translating by analogy. 1