Improving the neural network model in combination with a big semantic-enriched corpus for building an English - Vietnamese semantic-oriented machine translation system

Abstract

Although the machine translation (MT) quality has been significantly improved, it still fails to meet the practical use requirements, especially in the narrow fields of expertise and under-resource languages. To solve this problem, most studies have been focusing on improving algorithms, translation models and corpora. However, very few studies could address a very important aspect that greatly affects the translation quality, which is semantic-oriented translation. In this article, we propose a solution of building a context-based semantic-oriented MT system by improving the neural network translation model in combination with a big semantic-enriched corpus. The neural network translation approach allows understanding the semantics of the whole sentence based on context vector and phrase translation memory. Moreover, automatic translation results are pre-processed by enriching well-defined meanings to entities for creating the final translated text showing to users. This solution has been used to build an English-Vietnamese semantic-oriented machine translation system dedicated in the tourism field. The result shows that this solution gives good translations that are very helpful and useful to users.