1 citations · 2 across the 6 of their papers we have counts for
9 papers
Accuracy on In-Domain Samples Matters When Building Out-of-Domain detectors: A Reply to Marek et al. (2021)
Yinhe Zheng, Guanyi Chen
We have noticed that Marek et al. (2021) try to re-implement our paper Zheng et al. (2020a) in their work "OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation"…
Non-neural Models Matter: A Re-evaluation of Neural Referring Expression Generation Systems
Fahime Same, Guanyi Chen, Kees van Deemter
In recent years, neural models have often outperformed rule-based and classic Machine Learning approaches in NLG. These classic approaches are now often disregarded, for example wh…
What can Neural Referential Form Selectors Learn?
Guanyi Chen, Fahime Same, Kees van Deemter
Despite achieving encouraging results, neural Referring Expression Generation models are often thought to lack transparency. We probed neural Referential Form Selection (RFS) model…
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…
Lessons from Computational Modelling of Reference Production in Mandarin and English
Guanyi Chen, Kees van Deemter
Referring expression generation (REG) algorithms offer computational models of the production of referring expressions. In earlier work, a corpus of referring expressions (REs) in…