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Tuning Statistical Machine Translation Parameters Using Perplexity
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Abstract: Statistical Machine Translation (SMT) involves many tasks including modeling,
training, decoding, and evaluation. In this work, we present a methodology
for optimizing the training process to get better translation quality using
the well known GIZA++ SMT toolkit. The methodology is based on adjusting the
parameters of GIZA++ that affect the generation of the translation model.
When applying the methodology, an average improvement of 7% has been achieved
in the translation quality.
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| URL |
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| Publication year |
2005
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| Organization Name |
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| Country |
United States
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| City |
Las Vegas, Nevada, USA
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| Publisher |
Name:
IEEE
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| serial title |
IEEE International Conference on Information Reuse and Integration (IEEE IRI-2005)
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| Web Page |
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| Author(s) from ARC |
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| Agris Categories |
Documentation and information
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| Proposed Agrovoc |
Statistical Machine Translation; parameter tuning; perplexity;
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| Publication Type |
Conference/Workshop
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