Adversarial Training for Commonsense Inference

Lis Pereira; Xiaodong Liu; Fei Cheng; Masayuki Asahara; Ichiro Kobayashi

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arXiv · 2005.08156

Adversarial Training for Commonsense Inference

Lis Pereira·Xiaodong Liu·Fei Cheng·Masayuki Asahara·Ichiro Kobayashi

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Abstract

We propose an AdversariaL training algorithm for commonsense InferenCE (ALICE). We apply small perturbations to word embeddings and minimize the resultant adversarial risk to regularize the model. We exploit a novel combination of two different approaches to estimate these perturbations: 1) using the true label and 2) using the model prediction. Without relying on any human-crafted features, knowledge bases, or additional datasets other than the target datasets, our model boosts the fine-tuning performance of RoBERTa, achieving competitive results on multiple reading comprehension datasets that require commonsense inference.

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BibTeX RIS

Lis Pereira, Xiaodong Liu, Fei Cheng, Masayuki Asahara, Ichiro Kobayashi. 2020-05-17. Adversarial Training for Commonsense Inference. https://arxiv.org/abs/2005.08156

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