Retrofitting Structure-aware Transformer Language Model for End Tasks

• URL: http://arxiv.org/abs/2009.07408v1
• Date: Wed, 16 Sep 2020 01:07:07 GMT
• Title: Retrofitting Structure-aware Transformer Language Model for End Tasks
• Authors: Hao Fei and Yafeng Ren and Donghong Ji
• Abstract summary: We consider retrofitting structure-aware Transformer language model for facilitating end tasks. Middle-layer structural learning strategy is leveraged for structure integration. Experimental results show that the retrofitted structure-aware Transformer language model achieves improved perplexity.
• Score: 34.74181162627023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider retrofitting structure-aware Transformer-based language model for facilitating end tasks by proposing to exploit syntactic distance to encode both the phrasal constituency and dependency connection into the language model. A middle-layer structural learning strategy is leveraged for structure integration, accomplished with main semantic task training under multi-task learning scheme. Experimental results show that the retrofitted structure-aware Transformer language model achieves improved perplexity, meanwhile inducing accurate syntactic phrases. By performing structure-aware fine-tuning, our model achieves significant improvements for both semantic- and syntactic-dependent tasks.

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