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

• URL: http://arxiv.org/abs/2212.07428v2
• Date: Fri, 16 Dec 2022 15:35:37 GMT
• Title: Towards Linguistically Informed Multi-Objective Pre-Training for Natural Language Inference
• Authors: Maren Pielka, Svetlana Schmidt, Lisa Pucknat, Rafet Sifa
• Abstract summary: We introduce a linguistically enhanced combination of pre-training methods for transformers. The pre-training objectives include POS-tagging, synset prediction based on semantic knowledge graphs, and parent prediction based on dependency parse trees. Our approach achieves competitive results on the Natural Language Inference task, compared to the state of the art.
• Score: 0.38233569758620045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a linguistically enhanced combination of pre-training methods for transformers. The pre-training objectives include POS-tagging, synset prediction based on semantic knowledge graphs, and parent prediction based on dependency parse trees. Our approach achieves competitive results on the Natural Language Inference task, compared to the state of the art. Specifically for smaller models, the method results in a significant performance boost, emphasizing the fact that intelligent pre-training can make up for fewer parameters and help building more efficient models. Combining POS-tagging and synset prediction yields the overall best results.

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