Abstract
Benefitting from the rapid development of artificial intelligence (AI) and deep learning, the machine translation task based on neural networks has achieved impressive performance in many high-resource language pairs. However, the neural machine translation (NMT) models still struggle in the translation task on agglutinative languages with complex morphology and limited resources. Inspired by the finding that utilizing the source-side linguistic knowledge can further improve the NMT performance, we propose a multi-source neural model that employs two separate encoders to encode the source word sequence and the linguistic feature sequences. Compared with the standard NMT model, we utilize an additional encoder to incorporate the linguistic features of lemma, part-of-speech (POS) tag, and morphological tag by extending the input embedding layer of the encoder. Moreover, we use a serial combination method to integrate the conditional information from the encoders with the outputs of the decoder, which aims to enhance the neural model to learn a high-quality context representation of the source sentence. Experimental results show that our approach is effective for the agglutinative language translation, which achieves the highest improvements of +2.4 BLEU points on Turkish–English translation task and +0.6 BLEU points on Uyghur–Chinese translation task.
Highlights
With the rapid development of artificial intelligence and deep learning, neural networks are widely applied to various fields ranging from computer vision [1,2], speech recognition [3,4], and natural language processing (NLP) [5,6,7,8]
Experimental results in Turkish–English and Uyghur–Chinese machine translation tasks show that the proposed approach can effectively improve the translation performance of the agglutinative
The experimental results show that the proposed approach is capable of effectively improving the translation performance for agglutinative languages
Summary
With the rapid development of artificial intelligence and deep learning, neural networks are widely applied to various fields ranging from computer vision [1,2], speech recognition [3,4], and natural language processing (NLP) [5,6,7,8]. The standard neural machine translation (NMT) model [9,10,11,12] employs the encoder to map the source sentence into a continuous representation vector, it feeds the resulting vector to the decoder to generate the target sentence, which directly learns the translation relationship between two distinct languages from the bilingual parallel sentence pairs. Existing NMT models still struggle in the translation task of agglutinative languages with complex morphology and limited resources, such as Turkish to English and Uyghur to Chinese. There are many rare and out-of-vocabulary (OOV) words in the training process, which leads to many inaccurate translation results [17] and increases the NMT model complexity
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