Name
Title | ||
|---|---|---|
Egyptian Arabic to English Statistical Machine Translation System for NIST OpenMT'2015. |
Abstract | ||
|---|---|---|
The paper describes the Egyptian Arabic-to-English statistical machine translation (SMT) system that the QCRI-Columbia-NYUAD (QCN) group submitted to the NIST OpenMTu00272015 competition. The competition focused on informal dialectal Arabic, as used in SMS, chat, and speech. Thus, our efforts focused on processing and standardizing Arabic, e.g., using tools such as 3arrib and MADAMIRA. We further trained a phrase-based SMT system using state-of-the-art features and components such as operation sequence model, class-based language model, sparse features, neural network joint model, genre-based hierarchically-interpolated language model, unsupervised transliteration mining, phrase-table merging, and hypothesis combination. Our system ranked second on all three genres. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computation and Language | Ranking,Computer science,Machine translation,Phrase,Speech recognition,NIST,Artificial intelligence,Natural language processing,Artificial neural network,Egyptian Arabic,Language model,Transliteration |
DocType | Volume | Citations |
Journal | abs/1606.05759 | 0 |
PageRank | References | Authors |
0.34 | 25 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hassan Sajjad | 1 | 273 | 31.21 |
| Nadir Durrani | 2 | 344 | 36.04 |
| Francisco Guzmán | 3 | 84 | 7.03 |
| Preslav I. Nakov | 4 | 1771 | 138.66 |
| Ahmed Abdelali | 5 | 152 | 25.84 |
| Stephan Vogel | 6 | 108 | 11.07 |
| Wael Salloum | 7 | 59 | 6.86 |
| Ahmed El Kholy | 8 | 182 | 9.82 |
| Nizar Habash | 9 | 1833 | 145.59 |