88 citations · 131 across the 21 of their papers we have counts for
4 papers · 1 filter
Assessing Neural Referential Form Selectors on a Realistic Multilingual Dataset
Guanyi Chen, Fahime Same, Kees van Deemter
Previous work on Neural Referring Expression Generation (REG) all uses WebNLG, an English dataset that has been shown to reflect a very limited range of referring expression (RE) u…
Understanding the Use of Quantifiers in Mandarin
Guanyi Chen, Kees van Deemter
We introduce a corpus of short texts in Mandarin, in which quantified expressions figure prominently. We illustrate the significance of the corpus by examining the hypothesis (know…
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…