2,093 research outputs found
Adversarial Training for Commonsense Inference
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.Comment: 6 pages, Accepted to ACL2020 RepL4NLP worksho
TextGAIL: Generative Adversarial Imitation Learning for Text Generation
Generative Adversarial Networks (GANs) for text generation have recently
received many criticisms, as they perform worse than their MLE counterparts. We
suspect previous text GANs' inferior performance is due to the lack of a
reliable guiding signal in their discriminators. To address this problem, we
propose a generative adversarial imitation learning framework for text
generation that uses large pre-trained language models to provide more reliable
reward guidance. Our approach uses contrastive discriminator, and proximal
policy optimization (PPO) to stabilize and improve text generation performance.
For evaluation, we conduct experiments on a diverse set of unconditional and
conditional text generation tasks. Experimental results show that TextGAIL
achieves better performance in terms of both quality and diversity than the MLE
baseline. We also validate our intuition that TextGAIL's discriminator
demonstrates the capability of providing reasonable rewards with an additional
task.Comment: AAAI 202
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