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We introduce a linguistically enhanced combination of pre-training methodsfor transformers. The pre-training objectives include POS-tagging, synsetprediction based on semantic knowledge graphs, and parent prediction based ondependency parse trees. Our approach achieves competitive results on theNatural Language Inference task, compared to the state of the art. Specificallyfor smaller models, the method results in a significant performance boost,emphasizing the fact that intelligent pre-training can make up for fewerparameters and help building more efficient models. Combining POS-tagging andsynset prediction yields the overall best results.

Towards Linguistically Informed Multi-Objective Pre-Training for Natural Language Inference