Weighted Training for Cross-Task Learning

• URL: http://arxiv.org/abs/2105.14095v1
• Date: Fri, 28 May 2021 20:27:02 GMT
• Title: Weighted Training for Cross-Task Learning
• Authors: Shuxiao Chen, Koby Crammer, Hangfeng He, Dan Roth, Weijie J. Su
• Abstract summary: We introduce Target-Aware Weighted Training (TAWT), a weighted training algorithm for cross-task learning. We show that TAWT is easy to implement, is computationally efficient, requires little hyper parameter tuning, and enjoys non-asymptotic learning-theoretic guarantees. As a byproduct, the proposed representation-based task distance allows one to reason in a theoretically principled way about several critical aspects of cross-task learning.
• Score: 71.94908559469475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduce Target-Aware Weighted Training (TAWT), a weighted training algorithm for cross-task learning based on minimizing a representation-based task distance between the source and target tasks. We show that TAWT is easy to implement, is computationally efficient, requires little hyperparameter tuning, and enjoys non-asymptotic learning-theoretic guarantees. The effectiveness of TAWT is corroborated through extensive experiments with BERT on four sequence tagging tasks in natural language processing (NLP), including part-of-speech (PoS) tagging, chunking, predicate detection, and named entity recognition (NER). As a byproduct, the proposed representation-based task distance allows one to reason in a theoretically principled way about several critical aspects of cross-task learning, such as the choice of the source data and the impact of fine-tuning

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