Name
Abstract | ||
|---|---|---|
Reordering models are one of essential components of statistical machine translation. In this paper, we propose a topic-based reordering model to predict orders for neighboring blocks by capturing topic-sensitive reordering patterns. We automatically learn reordering examples from bilingual training data, which are associated with document-level and word-level topic information induced by LDA topic model. These learned reordering examples are used as evidences to train a topic-based reordering model that is built on a maximum entropy (MaxEnt) classifier. We conduct large-scale experiments to validate the effectiveness of the proposed topic-based reordering model on the NIST Chinese-to-English translation task. Experimental results show that our topic-based reordering model achieves significant performance improvement over the conventional reordering model using only lexical information. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-662-45924-9_37 | NLPCC |
Field | DocType | Citations |
Computer science,Synonym,Machine translation,Exploit,Speech recognition,Natural language processing,Artificial intelligence,Extensibility,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 14 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xing Wang | 1 | 58 | 10.07 |
| Deyi Xiong | 2 | 845 | 67.74 |
| Min Zhang | 3 | 1849 | 157.00 |
| Yu Hong | 4 | 246 | 35.44 |
| Jianmin Yao | 5 | 0 | 1.01 |