acl acl2012 acl2012-140 acl2012-140-reference knowledge-graph by maker-knowledge-mining
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Author: Graham Neubig ; Taro Watanabe ; Shinsuke Mori ; Tatsuya Kawahara
Abstract: In this paper, we demonstrate that accurate machine translation is possible without the concept of “words,” treating MT as a problem of transformation between character strings. We achieve this result by applying phrasal inversion transduction grammar alignment techniques to character strings to train a character-based translation model, and using this in the phrase-based MT framework. We also propose a look-ahead parsing algorithm and substring-informed prior probabilities to achieve more effective and efficient alignment. In an evaluation, we demonstrate that character-based translation can achieve results that compare to word-based systems while effectively translating unknown and uncommon words over several language pairs.
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