Sequence Level Mixed Sample Data Augmentation
EMNLP 2020, pp. 5547-5552, 2020.
摘要:
Despite their empirical success, neural networks still have difficulty capturing compositional aspects of natural language. This work proposes a simple data augmentation approach to encourage compositional behavior in neural models for sequence-to-sequence problems. Our approach, SeqMix, creates new synthetic examples by softly combining ...更多
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