Formalization for Example-based Machine Translation Example-based machine translation(EBMT)systems,so far,rely on heuristic measures in retrieving translation examples.Such a heuristic measure costs time to adjust,and might make its algorithm unclear.This paper presents a probabilistic model for EBMT.Under the proposed model,the system searches the translation example combination which has the highest probability.The proposed model clearly formalizes EBMT process.In addition,the model can naturally incorporate the context similarity of translation examples.The experimental results demonstrate that the proposed model has a slightly better translation quality than state-of-the-art EBMT systems.
This paper proposes a method of automatic back transliteration of proper nouns, in which a Japanese transliterated-word is restored to the original English word. The English words are created from a sequence of letters; thus our method can create new English words that are not registered in dictionaries or English word lists. When a katakana character is converted into English letters, there are various candidates of alphabetic characters. To ensure adequate conversion, the proposed method uses a target English context to calculate the probability of an English character or string corresponding to a Japanese katakana character or string. We confirmed the effectiveness of using the target English context by an experiment of personal-name back transliteration.
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