335 citations · 409 across the 20 of their papers we have counts for
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Stylized Text Generation Using Wasserstein Autoencoders with a Mixture of Gaussian Prior
Amirpasha Ghabussi, Lili Mou, Olga Vechtomova
Wasserstein autoencoders are effective for text generation. They do not however provide any control over the style and topic of the generated sentences if the dataset has multiple…
Adaptive Fusion Techniques for Multimodal Data
Gaurav Sahu, Olga Vechtomova
Effective fusion of data from multiple modalities, such as video, speech, and text, is challenging due to the heterogeneous nature of multimodal data. In this paper, we propose ada…
Adversarial Learning on the Latent Space for Diverse Dialog Generation
Kashif Khan, Gaurav Sahu, Vikash Balasubramanian +2
Generating relevant responses in a dialog is challenging, and requires not only proper modeling of context in the conversation but also being able to generate fluent sentences duri…
Generating Sentences from Disentangled Syntactic and Semantic Spaces
Yu Bao, Hao Zhou, Shujian Huang +5
Variational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space. However, generating sentences from the continuous lat…
Distilling Task-Specific Knowledge from BERT into Simple Neural Networks
Raphael Tang, Yao Lu, Linqing Liu +3
In the natural language processing literature, neural networks are becoming increasingly deeper and complex. The recent poster child of this trend is the deep language representati…