Papers with adversarial training approach
Retrieval-Enhanced Adversarial Training for Neural Response Generation (P19-1)
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| Challenge: | Existing approaches to dialogue systems are labor-intensive and difficult to scale up. |
| Approach: | They propose a Retrieval-Enhanced Adversarial Training method for neural response generation that leverages an adversarial training paradigm while taking advantage of N-best response candidates from a retrieval-based system to construct the discriminator. |
| Outcome: | The proposed method outperforms the vanilla Seq2Seq model and conventional adversarial training approach on a large scale dataset. |
LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification (D19-1)
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| Challenge: | Existing text classification models are fragile and sensitive to simple perturbations. |
| Approach: | They propose a generator-classifier adversarial training approach to improve classification models . they use a large-scale lexical knowledge base to generate attacking examples . |
| Outcome: | The proposed approach outperforms strong baselines and reduces test errors on neural networks. |
FREE: Fast and Robust Vision Language Models with Early Exits (2025.findings-acl)
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| Challenge: | Vision-Language Models (VLMs) have shown remarkable performance improvements in Vision-language tasks, but their large size poses challenges for real-world applications. |
| Approach: | They propose an adversarial approach to train exit classifiers in Vision-Language Models that uses a transformer layer and a classifier to perform input-adaptive inference. |
| Outcome: | The proposed approach speeds up inference speed with minimal drop in performance by 1.51 while retaining comparable performance. |