Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IALP.2012.50 | Asian Language Processing |
Keywords | Field | DocType |
input sentence,modality-preserving phrase-based statistical machine,simple method,feature function,phrase-based statistical mt method,characteristic word,modality error,machine translation,language translation,natural language processing,text analysis | Rule-based machine translation,Example-based machine translation,Language translation,Computer science,Machine translation,Phrase,Machine translation software usability,Natural language processing,Artificial intelligence,Pattern recognition,Speech recognition,Transfer-based machine translation,Sentence | Conference |
ISSN | ISBN | Citations |
2159-1962 | 978-0-7695-4886-9 | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masamichi Ideue | 1 | 0 | 0.34 |
| Kazuhide Yamamoto | 2 | 207 | 39.66 |
| Masao Utiyama | 3 | 714 | 86.69 |
| Eiichiro SUMITA | 4 | 1466 | 190.87 |