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A Rule and Super Function-based Machine Translation System for Chinese-Japanese Causative Sentences

机译：基于规则和超功能的中日使役句机器翻译系统

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摘要

Machine translation (MT) can be generally classified as Rule-Based Machine Translation (RBMT) or Example-Based Machine Translation (EBMT) or Statistics-Based Machine Translation (SBMT), and each has numerous problems which remain unsolved. A rule-based machine translation system is an effective way to implement a machine translation system because of its extensibility and maintainability. However, it is generally disadvantageous in processing efficiency. In this paper, we present a hybrid approach based on fixed rules and Super Functions (SF) - Based method. SF is known for its lower costs, and is applicability to common users who do not have high demand on translation quality. Our purpose is to improve the processing efficiency and the quality of machine translation. In the present research, sufficient Chinese-Japanese causative sentence patterns have been employed as a language-database for experiment, which proves the suggested method can effectively improve translation quality within the range under discussion.
机译：机器翻译（MT）通常可分为基于规则的机器翻译（RBMT）或基于示例的机器翻译（EBMT）或基于统计的机器翻译（SBMT），并且每种都有许多尚未解决的问题。基于规则的机器翻译系统由于具有可扩展性和可维护性，因此是实现机器翻译系统的有效方法。然而，这通常在处理效率上是不利的。在本文中，我们提出了一种基于固定规则和基于超级功能（SF）的混合方法。 SF以其较低的成本而著称，并且适用于对翻译质量要求不高的普通用户。我们的目的是提高处理效率和机器翻译的质量。在本研究中，已将足够的中日因果关系句型用作实验的语言数据库，证明了所建议的方法可以有效地提高讨论范围内的翻译质量。

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