5 papers
Intrinsic Task-based Evaluation for Referring Expression Generation
Guanyi Chen, Fahime Same, Kees van Deemter
Recently, a human evaluation study of Referring Expression Generation (REG) models had an unexpected conclusion: on \textsc{webnlg}, Referring Expressions (REs) generated by the st…
Models of reference production: How do they withstand the test of time?
Fahime Same, Guanyi Chen, Kees van Deemter
In recent years, many NLP studies have focused solely on performance improvement. In this work, we focus on the linguistic and scientific aspects of NLP. We use the task of generat…
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…
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…