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Found 8 papers, 1 papers with code
Computational Interpretations of Recency for the Choice of Referring Expressions in Discourse
First, we discuss the most common linguistic perspectives on the concept of recency and propose a taxonomy of recency metrics employed in Machine Learning studies for choosing the form of referring expressions in discourse context.
Constructing Distributions of Variation in Referring Expression Type from Corpora for Model Evaluation
We add to this work, by introducing a method for exploring variation in human RE choice on the basis of longitudinal corpora - substantial corpora with a single human judgement (in the process of composition) per RE.
Intrinsic Task-based Evaluation for Referring Expression Generation
Recently, a human evaluation study of Referring Expression Generation (REG) models had an unexpected conclusion: on \textsc{webnlg}, Referring Expressions (REs) generated by the state-of-the-art neural models were not only indistinguishable from the REs in \textsc{webnlg} but also from the REs generated by a simple rule-based system.
Models of reference production: How do they withstand the test of time?
We conclude that GREC can no longer be regarded as offering a reliable assessment of models' ability to mimic human reference production, because the results are highly impacted by the choice of corpus and evaluation metrics.
Assessing Neural Referential Form Selectors on a Realistic Multilingual Dataset
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) use.
Non-neural Models Matter: A Re-evaluation of Neural Referring Expression Generation Systems
In recent years, neural models have often outperformed rule-based and classic Machine Learning approaches in NLG.
What can Neural Referential Form Selectors Learn?
Despite achieving encouraging results, neural Referring Expression Generation models are often thought to lack transparency.
A Linguistic Perspective on Reference: Choosing a Feature Set for Generating Referring Expressions in Context
This paper reports on a structured evaluation of feature-based Machine Learning algorithms for selecting the form of a referring expression in discourse context.
