Okita, Tsuyoshi, Toral, Antonio ORCID: 0000-0003-2357-2960 and van Genabith, Josef ORCID: 0000-0003-1322-7944 (2012) Topic modeling-based domain adaptation for system combination. In: ML4HMT-12 Workshop, 9 Dec 2012, Mumbai, India.
Abstract
This paper gives the system description of the domain adaptation team of Dublin City University for our participation in the system combination task in the Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT (ML4HMT-12). We used the results of unsupervised document classification as meta information to the system combination module. For the Spanish-English data, our strategy achieved 26.33 BLEU points, 0.33 BLEU points absolute improvement over the standard confusion-network-based system combination. This was the best score in terms of BLEU among six participants in ML4HMT-12.
Metadata
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Event Type: | Workshop |
Refereed: | No |
Uncontrolled Keywords: | Statistical Machine Translation; Topic Model; System Combination |
Subjects: | Computer Science > Machine translating |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing |
Use License: | This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License |
ID Code: | 17674 |
Deposited On: | 19 Dec 2012 11:35 by Tsuyoshi Okita . Last Modified 19 Jan 2022 12:47 |
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