A study on intelligent translation of English sentences by a semantic feature extractor

Abstract

Abstract In order to enhance the performance of machine translation, this article briefly introduced algorithms that can be used to extract semantic feature vectors. Then, the aforementioned algorithms were integrated with the encoder–decoder translation algorithm, and the resulting algorithms were subsequently tested. First, the performance of the semantic recognition of the long short-term memory (LSTM)-based semantic feature extractor was tested, followed by a comparison with the translation algorithm that does not include semantic features, as well as the translation algorithm that incorporates convolutional neural network-extracted semantic features. The findings demonstrated that the LSTM-based semantic feature extractor accurately identified the semantics of the source language. The proposed translation algorithm, which is based on LSTM semantic features, achieved more accurate translations compared to the other two algorithms. Furthermore, it was less affected by the length of the source language.