Title :
Modality-Preserving Phrase-Based Statistical Machine Translation
Author :
Ideue, M. ; Yamamoto, Koji ; Utiyama, M. ; Sumita, Eiichiro
Author_Institution :
Dept. of Electr. Eng., Nagaoka Univ. of Technol., Nagaoka, Japan
Abstract :
In machine translation (MT), modality errors are often critical. We propose a phrase-based statistical MT method that preserves the modality of input sentences. The method introduces a feature function that counts the number of phrases in a sentence that are characteristic words for modalities. This simple method increases the number of translations that have the same modality as the input sentences.
Keywords :
language translation; natural language processing; text analysis; feature function; input sentences; modality errors; modality preservation; modality-preserving phrase-based statistical machine translation; phrase-based statistical MT method; Accuracy; Equations; Feature extraction; Manuals; Mathematical model; Training; Training data; modality; statistical machine translation;
Conference_Titel :
Asian Language Processing (IALP), 2012 International Conference on
Conference_Location :
Hanoi
Print_ISBN :
978-1-4673-6113-2
Electronic_ISBN :
978-0-7695-4886-9
DOI :
10.1109/IALP.2012.50