In this research, we propose two different frameworks combining outputs from multiple on-line machine translation systems. We train the language model and translation model from IWSLT07 training data. The first framework consists of several modules, including selection, substitution, insertion, and deletion. In the second framework, after selection, we use a maximum entropy classifier to classify each word in the selected hypothesis according to Damerau-Levenshtein distance. According to these classification results, each word in the selected hypothesis are processed with different post-processing. We evaluate these combination frameworks on IWSLT07 task. It contains tourism-related sentences. The translation direction is from Chinese to English in our test set. Three on-line machine translation systems, Google, Yahoo, and TransWhiz are used in the investigation. The experimental results show that first combination framework improves BLEU score from 19.15% to 20.55%. The second combination framework improves BLEU from 19.15% to 20.47%. These frameworks achieves absolute improvement of 1.4% and 1.32% in BLEU score, respectively.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0908109-093951 |
| Date | 08 September 2009 |
| Creators | Chen, Yi-Chang |
| Contributors | Jing-Shin Chang, Jeih-weih Hung, Chung-Hsien Wu, Hsin-Min Wang, Chia-Ping Chen |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0908109-093951 |
| Rights | not_available, Copyright information available at source archive |
Page generated in 0.002 seconds