Evaluation of ultra-large-scale English translation mechanism based on Bi-LSTM

How to effectively extract and utilise syntactic features in the model is an issue worthy of further study in the current translation quality estimation task. This paper introduces a Bi-directional Long-Short-Term Memory (Bi-LSTM)-based English translation mechanism evaluation model aimed at providing fast and accurate feedback to enhance machine translation systems. The proposed model incorporates the following strategies. Firstly, we utilise the Skip-gram model and the Continuous Bag of Words (CBOW) model of the Word2Vec to preprocess text data before feature extraction. Second, we utilise three types of translation feature to promote the performance of translation evaluation, including word prediction feature, word-embedding feature and syntactic structure feature. Third, we design an English translation mechanism evaluation model based on the Bi-LSTM model by fusing the three types of extracted features. The results of the experiment demonstrate that the approach suggested in this paper exhibits favourable evaluation performance.