Abstract
We achieved a state of the art performance in statistical machine translation by using a large number of features with an online large-margin training algorithm. The millions of parameters were tuned only on a small development set consisting of less than 1K sentences. Experiments on Arabic-to- English translation indicated that a model trained with sparse binary features outperformed a conventional SMT system with a small number of features.
| Original language | English |
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| Pages | 764-773 |
| Number of pages | 10 |
| Publication status | Published - 2007 |
| Externally published | Yes |
| Event | 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007 - Prague, Czech Republic Duration: 2007 Jun 28 → 2007 Jun 28 |
Other
| Other | 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007 |
|---|---|
| Country/Territory | Czech Republic |
| City | Prague |
| Period | 07/6/28 → 07/6/28 |
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Science Applications
- Information Systems