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Example-Based Translation on Massivcly Parallel Computers

大规模并行计算机上基于示例的翻译

基本信息

批准号：
05680292
负责人：
SATO Satoshi
金额：
$ 1.28万
依托单位：
Japan Advanced Institute of Science and Technology, Hokuriku
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1993
资助国家：
日本
起止时间：
1993 至 1994
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-05680292/
关键词：
Example-Based Translation Massively Parallel Computer Machine Translation Technical Terms 論文表題

项目摘要

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.

本研究的主要结果如下：1.收集了日语和英语的计算机科学专业术语，构建了包含7,000个双语专业术语（翻译实例）的翻译数据库.我们使用动态规划研究了最佳匹配检索方法。该方法检索与检索输入类似的多个翻译实例。本文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。我们使用并行最佳匹配检索程序在nCUBE 2上构建了一个专业术语翻译系统。该系统是第一个在商用大规模并行计算机上运行的机器翻译系统。该系统表现出较高的性能和良好的可扩展性：性能与处理器数量成正比，可扩展到百万级翻译实例的系统.我们在大规模并行计算机CM-5上构建了另一个翻译系统。该系统具有高性能和良好的可扩展性：比SS 2上的顺序翻译系统快15倍。通过研究，我们得出结论：基于实例的并行翻译具有高性能和良好的可扩展性，是一种很有前途的翻译方法。