  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 referring expressions in context
(REG-in-context) as a case study and start our analysis from GREC, a
comprehensive set of shared tasks in English that addressed this topic over a
decade ago. We ask what the performance of models would be if we assessed them
(1) on more realistic datasets, and (2) using more advanced methods. We test
the models using different evaluation metrics and feature selection
experiments. 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. Our results also suggest that pre-trained language models are less
dependent on the choice of corpus than classic Machine Learning models, and
therefore make more robust class predictions.
