Example-Based Translation on Massivcly Parallel Computers

本研究的主要成果如下：1。我们收集了日语和英语的计算机科学专业术语，并构建了包含7000个双语专业术语（翻译示例）的翻译数据库。研究了基于动态规划的最佳匹配检索方法。该方法检索与检索输入类似的几个翻译示例。研究了并行最佳匹配检索方法，并在大规模并行计算机nCUBE2.4上实现了该方法。我们利用并行最优匹配检索程序在nCUBE2上构建了一个专业术语翻译系统。该系统是商用大规模并行计算机上的第一个机器翻译系统。该系统表现出高性能和良好的可扩展性：性能与处理器数量成正比，并且可以扩展到具有数百万个翻译示例的系统。我们在大型并行计算机CM-5上构建了另一个翻译系统。它还显示出高性能和良好的可伸缩性：它比SS2上的顺序系统快大约15倍。从这项研究中，我们得出结论，并行基于实例的翻译是有前途的：它表现出高性能和良好的可扩展性。

The Major results of this research are as follows.1. We collected technical terms of Computer Science in Japanese and in English, and constructed translate database with 7,000 bilingual technical terms (translation examples).2. We investigated the best match retrival method using Dynamic Programming. This method retrieves several translation examples similar to the retrieval input.3. We investigated the parallel best match retriecval mcthod and implemented it on a massively parallel computer, nCUBE2.4. We constructed a translation system of technical terms on an nCUBE2 using the parallel best match retrieval program. This system is the first machine translation system on a commercial massively parallel computer. This system shows high performance and good scalability : the performance is proportional to the number of processors, and it can be scaled up to the system with some million translation examples.5. We constructed another translation system on a massively parallel computer, CM-5. It also shows high performance and good scalability : It is about fifteen times faster than the sequential system on a SS2.From this research, we concluded that parallel Example-Based Translation is promising : it shows high performance and good scalability.