实例机器翻译（EBMT）

Example-Based Machine Translation (EBMT) is an approach to machine translation proposed in the late 1980s by the Japanese scholar Makoto Nagao, which relies on examples of translated text (i.e., translation instances) to translate new text. This method is mainly based on the idea of "simulation", i.e., finding and utilizing translated examples that are similar to the new sentence to generate a translation. The following is a detailed description of EBMT's features, working principle, advantages and disadvantages.

Main Features

1. Example Database Dependency: The EBMT system relies on a large database of translation examples. These examples contain sentence pairs in various source and target languages.
2. Simulation and Adaptation: While translating, the system searches the database for examples that are most similar to the new sentence, and then modifies and adapts the found translations as needed to produce the final translation.
3. Incorporating Linguistic Knowledge: Although EBMT relies heavily on examples, it may incorporate some linguistic knowledge when dealing with lexical and syntactic adaptation.

Working Principle

1. Sentence decomposition: After receiving a new sentence in the source language, the EBMT system first breaks it down into smaller fragments, such as phrases or clauses.
2. Database Matching: The system searches for instances in the database that match these smaller fragments. This process usually relies on similarity scoring mechanisms such as edit distance or semantic similarity.
3. Sentence reorganization: Once a match is found, the system reassembles these translation fragments according to the syntactic and semantic rules of the target language to form a complete translated sentence.

Pros and Cons

Pros

• High flexibility: EBMT can handle non-standard expressions and rare phrases well because it relies on real translation examples.
• Contextual adaptability: by choosing translation instances of whole sentences or long phrases, EBMT can better preserve contextual information and improve the naturalness and accuracy of translation.

Cons：

• High resource requirements: building and maintaining a large-scale and high-quality translation instance database requires a lot of human and time resources.
• Coverage problem: If the database lacks instances similar to the new sentence, the translation quality may be significantly degraded.

Overall, the example translation system provides a unique translation method that is particularly suitable for dealing with complex or non-standard texts that are difficult to cope with by traditional translation methods. With the continuous improvement of the database and algorithms, EBMT still shows its unique advantages in some specific application areas.