3 citations · 8 across the 5 of their papers we have counts for
7 papers
On the Latent Holes of VAEs for Text Generation
Ruizhe Li, Xutan Peng, Chenghua Lin
In this paper, we provide the first focused study on the discontinuities (aka. holes) in the latent space of Variational Auto-Encoders (VAEs), a phenomenon which has been shown to…
Affective Decoding for Empathetic Response Generation
Chengkun Zeng, Guanyi Chen, Chenghua Lin +2
Understanding speaker's feelings and producing appropriate responses with emotion connection is a key communicative skill for empathetic dialogue systems. In this paper, we propose…
Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation
Ruizhe Li, Xiao Li, Guanyi Chen +1
The Variational Autoencoder (VAE) is a popular and powerful model applied to text modelling to generate diverse sentences. However, an issue known as posterior collapse (or KL loss…
DGST: a Dual-Generator Network for Text Style Transfer
Xiao Li, Guanyi Chen, Chenghua Lin +1
We propose DGST, a novel and simple Dual-Generator network architecture for text Style Transfer. Our model employs two generators only, and does not rely on any discriminators or p…
A Stable Variational Autoencoder for Text Modelling
Ruizhe Li, Xiao Li, Chenghua Lin +2
Variational Autoencoder (VAE) is a powerful method for learning representations of high-dimensional data. However, VAEs can suffer from an issue known as latent variable collapse (…
Latent Space Factorisation and Manipulation via Matrix Subspace Projection
Xiao Li, Chenghua Lin, Ruizhe Li +2
We tackle the problem disentangling the latent space of an autoencoder in order to separate labelled attribute information from other characteristic information. This then allows u…