Papers by Peter Chin
Semi-supervised Adversarial Text Generation based on Seq2Seq models (2022.emnlp-industry)
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| Challenge: | In contrast, adversarial training has been used in computer vision to improve models’ robustness due to the discrete nature of text. |
| Approach: | They propose a way to generate adversarial samples by using pseudo-labeled in-domain text data to train a seq2seq model for adversarials and combine it with paraphrase detection. |
| Outcome: | The proposed model generates realistic and relevant adversarial samples compared to other state-of-the-art models and recovers up to 70% of errors. |
RetroGAN: A Cyclic Post-Specialization System for Improving Out-of-Knowledge and Rare Word Representations (2021.findings-acl)
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| Challenge: | Retrofitting is a technique used to move word vectors closer together or further apart in their space to reflect their relationships in a Knowledge Base (KB). |
| Approach: | They propose a system that uses two GANs to learn a one-to-one mapping between concepts and retrofitted counterparts. |
| Outcome: | The proposed system performs well on word-similarity benchmarks and a sentence simplification task. |
Sound Signal Processing with Seq2Tree Network (L18-1)
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| Challenge: | Recent LSTM models have been used to model sequential data processing tasks because of their ability to preserve previous information weighted on distance. |
| Approach: | They propose to use a tree-structured tree-based neural network architecture to solve the problem of unbalanced connections between data units inside and outside semantic groups. |
| Outcome: | The proposed model outperforms the state-of-the-art Bidirectional LSTM model on a signal and noise separation task. |