Name
Title | ||
|---|---|---|
Exploiting Out-of-Domain Data Sources for Dialectal Arabic Statistical Machine Translation |
Abstract | ||
|---|---|---|
Statistical machine translation for dialectal Arabic is characterized by a lack of data since data acquisition involves the transcription and translation of spoken language. In this study we develop techniques for extracting parallel data for one particular dialect of Arabic (Iraqi Arabic) from out-of-domain corpora in different dialects of Arabic or in Modern Standard Arabic. We compare two different data selection strategies (cross-entropy based and submodular selection) and demonstrate that a very small but highly targeted amount of found data can improve the performance of a baseline machine translation system. We furthermore report on preliminary experiments on using automatically translated speech data as additional training data. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Example-based machine translation,Arabic,Computer science,Data acquisition,Machine translation,Submodular set function,Speech recognition,Modern Standard Arabic,Machine translation software usability,Artificial intelligence,Natural language processing,Spoken language |
DocType | Volume | Citations |
Journal | abs/1509.01938 | 0 |
PageRank | References | Authors |
0.34 | 17 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Katrin Kirchhoff | 1 | 1026 | 95.24 |
| bing zhao | 2 | 0 | 0.34 |
| Wen Wang | 3 | 106 | 11.93 |