| Challenge: | Existing generative adversarial networks suffer from the instability of reinforcement learning training algorithms such as policy gradient, leading to unstable performance. |
| Approach: | They propose a framework where the discriminator assigns rewards to samples acquired from a stationary distribution near the data rather than the generator’s distribution. |
| Outcome: | The proposed framework outperforms state-of-the-art text GANs with a more stable training process. |
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| Challenge: | Existing text generation methods tend to produce repeated and ”boring” expressions. |
| Approach: | They propose a model that assigns low reward for repeatedly generated text and high reward for ”novel” and fluent text, and a novel language-model based discriminator which can distinguish novel text from repeated text without the saturation problem. |
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Best Student Forcing: A Simple Training Mechanism in Adversarial Language Generation (2020.lrec-1)
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Generative Adversarial Training with Perturbed Token Detection for Model Robustness (2023.emnlp-main)
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GAN-BERT: Generative Adversarial Learning for Robust Text Classification with a Bunch of Labeled Examples (2020.acl-main)
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| Challenge: | Recent Transformer-based architectures provide impressive results in many NLP tasks, but obtaining high-quality annotated data is expensive and time consuming. |
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Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement Learning (2021.eacl-main)
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| Challenge: | Existing approaches to language modeling use autoregressive methods, but they can produce repetitive results. |
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ReinforceBug: A Framework to Generate Adversarial Textual Examples (2021.naacl-main)
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| Challenge: | Recent studies have demonstrated that ML Models are sensitive to Adversarial Examples (AEs) AEs are generated by perturbingining examples that preserve the intrinsic utility of the ML solutions but influence the classifier's predictions between original and modified inputs. |
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| Challenge: | Recent work has shown that models can be easily fooled by intentionally designed adversarial examples. |
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End-to-end Adversarial Sample Generation for Data Augmentation (2023.findings-emnlp)
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