Papers by Xinwei Shen
TILGAN: Transformer-based Implicit Latent GAN for Diverse and Coherent Text Generation (2021.findings-acl)
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| Challenge: | Existing autoregressive models suffer from the exposure bias problem due to mismatches between training and generation stages. |
| Approach: | They propose a Transformerbased Implicit Latent GAN which combines a transformer autoencoder and GAN in the latent space with a novel design and distribution matching based on the Kullback-Leibler divergence. |
| Outcome: | The proposed model improves local and global coherence and quality-diversity trade-off on three benchmark datasets. |