Adversarial Training for Commonsense Inference

By L. Pereira, Xiaodong Liu, Fei Cheng, Masayuki Asahara, Ichiro Kobayashi. 2020

32 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Xiang Ren
2. Nanyun Peng
3. Rujun Han
4. Yejin Choi
5. Kai-Wei Chang
6. Jie Yang
7. Hannaneh Hajishirzi
8. Percy Liang

Citations in the corpus

• ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning (2020, methodology): “Recent approaches leveraging large pre-trained language models (PTLMs) achieved state-of-the-art results on a range of event temporal reasoning tasks (Ning et al., 2020; Pereira et al., 2020; Wang et al., 2020; Zhou et al., 2020c; Han et al., 2019b).”