Papers with Variational Autoencoders
On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation (D19-56)
Copied to clipboard
| Challenge: | Variational Autoencoders suffer from learning uninformative latent representations due to issues such as approximated posterior collapse or entanglement of the latent space. |
| Approach: | They propose to impose an explicit constraint on the Kullback-Leibler divergence term inside the VAE objective function to understand the significance of the KL term in controlling the information transmitted through the VAe channel. |
| Outcome: | The proposed constraint avoids posterior collapse, but it also controls the information transmitted through the VAE channel. |
A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User Reviews (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing topic models may extract topics associated with writers’ subjective opinions mixed with those related to factual descriptions. |
| Approach: | They propose a neural topic model combined with adversarial training to disentangle opinion topics from plot and neutral ones. |
| Outcome: | The proposed model shows improved coherence and variety of topics, consistent disentanglement rate, and superior sentiment classification performance to other supervised topic models. |
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation (2022.acl-long)
Copied to clipboard
Wei Chen, Yeyun Gong, Song Wang, Bolun Yao, Weizhen Qi, Zhongyu Wei, Xiaowu Hu, Bartuer Zhou, Yi Mao, Weizhu Chen, Biao Cheng, Nan Duan
| Challenge: | Existing pre-trained dialog models shed light on various downstream tasks in natural language processing (NLP). |
| Approach: | They propose a dialog pre-training framework that introduces latent variables into the enhanced encoder-decoder pre-train framework to increase relevance and diversity of responses. |
| Outcome: | The proposed model achieves state-of-the-art on personaChat, DailyDialog, and DSTC7-AVSD datasets. |
Empirical Prior for Text Autoencoders (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Variational Autoencoders (VAE) are used to train generative models with latent variables. |
| Approach: | They propose a transition from Variational Autoencoders (VAE) to text autoencodeurs (AE) which model a compact latent space and preserves the capability of the language model itself. |
| Outcome: | The proposed method generates higher quality and more diverse text than the VAE-based Transformer baselines, and is more efficient than previous approaches. |