Improving Multilingual Neural Machine Translation by Utilizing Semantic and Linguistic Features
Mengyu Bu, Shuhao Gu, Yang Feng · 2024
The many-to-many multilingual neural machine translation can be regarded as the process of integrating semantic features from the source sentences and linguistic features from the target sentences.To enhance zero-shot translation, models need to share knowledge across languages, which can be achieved through auxiliary tasks for learning a universal representation or cross-lingual mapping.To this end, we propose to exploit both semantic and linguistic features between multiple languages to enhance multilingual translation.On the encoder side, we introduce a disentangling learning task that aligns encoder representations by disentangling semantic and linguistic features, thus facilitating knowledge transfer while preserving complete information.On the decoder side, we leverage a linguistic encoder to integrate low-level linguistic features to assist in the target language generation.Experimental results on multilingual datasets demonstrate significant improvement in zero-shot translation compared to the baseline system, while maintaining performance in supervised translation.Further analysis validates the effectiveness of our method in leveraging both semantic and linguistic features.