Selection of In-Domain Bilingual Sentence Pairs Based on Topic Information

Abstract EN

The performance of a machine translation system (MTS) depends on the quality and size of the training data.

How to extend the training dataset for the MTS in specific domains with effective methods to enhance the performance of machine translation needs to be explored.

A method for selecting in-domain bilingual sentence pairs based on the topic information is proposed.

With the aid of the topic relevance of the bilingual sentence pairs to the target domain, subsets of sentence pairs related to the texts to be translated are selected from a large-scale bilingual corpus to train the translation system in specific domains to improve the translation quality for in-domain texts.

Through the test, the bilingual sentence pairs are selected by using the proposed method, and further the MTS is trained.

In this way, the translation performance is greatly enhanced.