  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. The code is available at https://github.com/ictnlp/SemLing-MNMT.
