Performance on a dataset is often regarded as the key criterion for assessing NLP models. I
argue for a broader perspective, which emphasizes scientific explanation. I draw on a long …

In the task of entity description generation, given a context and a specified entity, a model
must describe that entity correctly and in a contextually-relevant way. In this task, as well as …

In recent years, a range of Neural Referring Expression Generation (REG) systems have
been built and they have often achieved encouraging results. However, these models are …

This paper reports on a structured evaluation of feature-based Machine Learning algorithms
for selecting the form of a referring expression in discourse context. Based on this …

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 generating …

A key task in natural language generation (NLG) is Referring Expression Generation (REG),
in which a set of properties are selected to describe a target referent. Computational …

Abstract Data-to-text Natural Language Generation (NLG) is the computational process of
generating natural language in the form of text or voice from non-linguistic data. A core micro …

Referring expression generation (REG) algorithms offer computational models of the
production of referring expressions. In earlier work, a corpus of referring expressions (REs) …

Despite achieving encouraging results, neural Referring Expression Generation models are
often thought to lack transparency. We probed neural Referential Form Selection (RFS) …

Recently, a human evaluation study of Referring Expression Generation (REG) models had
an unexpected conclusion: on\textsc {webnlg}, Referring Expressions (REs) generated by …