11 citations · 22 across the 5 of their papers we have counts for
5 papers
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…
A Text Reassembling Approach to Natural Language Generation
Xiao Li, Kees van Deemter, Chenghua Lin
Recent years have seen a number of proposals for performing Natural Language Generation (NLG) based in large part on statistical techniques. Despite having many attractive features…
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…
A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification
Ruizhe Li, Chenghua Lin, Matthew Collinson +2
Recognising dialogue acts (DA) is important for many natural language processing tasks such as dialogue generation and intention recognition. In this paper, we propose a dual-atten…