NotesFAQContact Us
Search Tips
ERIC Number: ED529483
Record Type: Non-Journal
Publication Date: 2011
Pages: 131
Abstractor: As Provided
Reference Count: 0
ISBN: ISBN-978-1-1246-2045-9
Parameterizing Phrase Based Statistical Machine Translation Models: An Analytic Study
Cer, Daniel
ProQuest LLC, Ph.D. Dissertation, University of Colorado at Boulder
The goal of this dissertation is to determine the best way to train a statistical machine translation system. I first develop a state-of-the-art machine translation system called Phrasal and then use it to examine a wide variety of potential learning algorithms and optimization criteria and arrive at two very surprising results. First, despite the strong intuitive appeal of more recent evaluation metrics, training to these metrics is no better than the older traditional approach of training to BLEU. Second, the most widely used learning algorithm for training machine translation systems, called minimum error rate training (MERT), works no better than standard machine learning algorithms such as log-linear models. This result demonstrates that machine translation does not require using a special purpose learning algorithm, but rather can be approached in a manner similar to other natural language processing and machine learning tasks. These results have a number of important implications. Contrary to existing beliefs, work on improving machine translation evaluation metrics and then training to the improved metrics will not in itself result in improved translation systems. Even more significantly, the widespread usage of MERT has limited the sort of models that can be used for machine translation, as it does not scale well to large numbers of features. If it is not necessary to use MERT to train competitive systems, machine translation can be treated similarly to any other natural language processing task with models that include arbitrarily large feature sets. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page:]
ProQuest LLC. 789 East Eisenhower Parkway, P.O. Box 1346, Ann Arbor, MI 48106. Tel: 800-521-0600; Web site:
Publication Type: Dissertations/Theses - Doctoral Dissertations
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A