Abstract
Statistical models for reordering source words have been used to enhance hierarchical phrase-based statistical machine translation. There are existing word-reordering models that learn reorderings for any two source words in a sentence or only for two contiguous words. This paper proposes a series of separate sub-models to learn reorderings for word pairs with different distances. Our experiments demonstrate that reordering sub-models for word pairs with distances less than a specific threshold are useful to improve translation quality. Compared with previous work, our method more effectively and efficiently exploits helpful word-reordering information; it improves a basic hierarchical phrase-based system by 2.4-3.1 BLEU points and keeps the average time of translating one sentence under 10 s.
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Notes
In translation experiments, we also tried adding a new penalty feature (how many source words in the input sentence are unaligned) to penalize unaligned words. However, this feature did not influence translation performance significantly.
Note that these scores are correspondingly calculated for different sub-models \(M_n\) and the sub-model weights are tuned separately.
In the original Hiero paper (Chiang 2005), only two nonterminals are allowed. However, it is not theoretically impossible to create rules with more than two nonterminals, hence our use of K here.
As we are using a cache, memory usage is a concern, but the size of the cache for each sentence is negligible compared to the size of the translation and language models, and thus the memory footprint is not increased significantly.
Note that “4” and “5” in the source and target sentences are original source and target words. This sentence pair is from a patent-translation corpus and there is a figure in the article, where the light source is labeled as 4 and the optical fiber is labeled as 5.
Cache was used in all experiments.
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Acknowledgments
Hai Zhao was partially supported by the National Natural Science Foundation of China (Grant No. 61170114, and Grant No. 61272248), the National Basic Research Program of China (Grant No. 2013CB329401), the Science and Technology Commission of Shanghai Municipality (Grant No. 13511500200), the European Union Seventh Framework Program (Grant No. 247619), the Cai Yuanpei Program (CSC fund 201304490199, 201304490171), and the art and science interdisciplinary funds of Shanghai Jiao Tong University, No. 14X190040031, and the Key Project of National Society Science Foundation of China, No. 15-ZDA041.
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Zhang, J., Utiyama, M., Sumita, E. et al. Learning local word reorderings for hierarchical phrase-based statistical machine translation. Machine Translation 30, 1–18 (2016). https://doi.org/10.1007/s10590-016-9178-7
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DOI: https://doi.org/10.1007/s10590-016-9178-7