篇名 | Fertility-based Source-Language-biased Inversion Transduction Grammar for Word Alignment |
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卷期 | 14:1 |
作者 | Huang, Chung-chi 、 Chang, S. Jason |
頁次 | 001-017 |
關鍵字 | Inversion Transduction Grammar 、 Word Alignment 、 Syntax-based Statistical Translation Model 、 THCI Core |
出刊日期 | 200903 |
We propose a version of Inversion Transduction Grammar (ITG) model with
IBM-style notation of fertility to improve word-alignment performance. In our approach, binary context-free grammar rules of the source language, accompanied by orientation preferences of the target language and fertilities of words, are leveraged to construct a syntax-based statistical translation model. Our model, inherently possessing the characteristics of ITG restrictions and allowing for many consecutive words aligned to one and vice-versa, outperforms the Bracketing Transduction Grammar (BTG) model and GIZA++, a state-of-the-art word aligner, not only in alignment error rate (23% and 14% error reduction) but also in consistent phrase error rate (13% and 9% error reduction). Better performance in these two evaluation metrics suggests that, based on our word alignment result, more accurate phrase pairs may be acquired, leading to better machine translation quality.