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>Predicate-Argument Structure-based Preordering for Japanese-English Statistical Machine Translation of Scientific Papers
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Predicate-Argument Structure-based Preordering for Japanese-English Statistical Machine Translation of Scientific Papers
Translating Japanese to English is difficult because they belong to different language families. Naieve phrase-based statistical machine translation (SMT) often fails to address syntactic difference between Japanese and English. Preordering methods are one of the simple but effective approaches that can model reordering in a long distance, which is crucial in translating Japanese and English. Thus, we apply a predicate-argument structure-based preordering method to the Japanese-English statistical machine translation task of scientific papers. Our method is based on the method described in (Hoshino et al., 2013), and extends their rules to handle abbreviation and passivization frequently found in scientific papers. Experimental results show that our proposed method improves performance of both (Hoshino et al., 2013)'s system and our phrase-based SMT baseline without preordering.
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机译：日语很难翻译成英语，因为它们属于不同的语言家族。单纯的基于短语的统计机器翻译（SMT）通常无法解决日语和英语之间的句法差异。预排序方法是一种简单而有效的方法，可以在很长的距离内对重新排序进行建模，这对于翻译日语和英语至关重要。因此，我们将基于谓词参数结构的预排序方法应用于科学论文的日英统计机器翻译任务。我们的方法基于（Hoshino et al。，2013）中描述的方法，并扩展了它们的规则以处理科学论文中经常出现的缩写和钝化。实验结果表明，我们提出的方法可以提高系统的性能（Hoshino等，2013），而无需预先排序即可改善基于短语的SMT基线。
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