Learning Phrase Translation using Level of Detail Approach

Hendra Setiawan, Haizhou Li, Min Zhang


Abstract
We propose a simplified Level Of Detail (LOD) algorithm to learn phrase translation for statistical machine translation. In particular, LOD learns unknown phrase translations from parallel texts without linguistic knowledge. LOD uses an agglomerative method to attack the combinatorial explosion that results when generating candidate phrase translations. Although LOD was previously proposed by (Setiawan et al., 2005), we improve the original algorithm in two ways: simplifying the algorithm and using a simpler translation model. Experimental results show that our algorithm provides comparable performance while demonstrating a significant reduction in computation time.
Anthology ID:
2005.mtsummit-papers.32
Volume:
Proceedings of Machine Translation Summit X: Papers
Month:
September 13-15
Year:
2005
Address:
Phuket, Thailand
Venue:
MTSummit
SIG:
Publisher:
Note:
Pages:
243–250
Language:
URL:
https://aclanthology.org/2005.mtsummit-papers.32
DOI:
Bibkey:
Cite (ACL):
Hendra Setiawan, Haizhou Li, and Min Zhang. 2005. Learning Phrase Translation using Level of Detail Approach. In Proceedings of Machine Translation Summit X: Papers, pages 243–250, Phuket, Thailand.
Cite (Informal):
Learning Phrase Translation using Level of Detail Approach (Setiawan et al., MTSummit 2005)
Copy Citation:
PDF:
https://aclanthology.org/2005.mtsummit-papers.32.pdf