In spite of the core role human judgement plays in evaluating the performance of NLP systems, the way human assessments are elicited in NLP experiments, and to some extent the nature of human judgement itself, pose challenges to the reliability and validity of human evaluation. In the context of the larger ReproHum project, aimed at running large scale multi-lab reproductions of human judgement, we replicated the understandability assessment by humans on several generated outputs of simplified text described in the paper “Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table” by Shardlow and Nawaz, appeared in the Proceedings of ACL 2019. Although we had to implement a series of modifications compared to the original study, which were necessary to run our human evaluation on exactly the same data, we managed to collect assessments and compare results with the original study. We obtained results consistent with those of the reference study, confirming their findings. The paper is complete with as much information as possible to foster and facilitate future reproduction.

Automatic methods for generating and gathering linguistic data have proven effective for fine-tuning Language Models (LMs) in languages less resourced than English. Still, while there has been emphasis on data quantity, less attention has been given to its quality. In this work, we investigate the impact of human intervention on machine-generated data when fine-tuning dialogical models. In particular, we study (1) whether post-edited dialogues exhibit higher perceived quality compared to the originals that were automatically generated; (2) whether fine-tuning with post-edited dialogues results in noticeable differences in the generated outputs; and (3) whether post-edited dialogues influence the outcomes when considering the parameter size of the LMs. To this end we created HED-IT, a large-scale dataset where machine-generated dialogues are paired with the version post-edited by humans. Using both the edited and unedited portions of HED-IT, we fine-tuned three different sizes of an LM. Results from both human and automatic evaluation show that the different quality of training data is clearly perceived and it has an impact also on the models trained on such data. Additionally, our findings indicate that larger models are less sensitive to data quality, whereas this has a crucial impact on smaller models. These results enhance our comprehension of the impact of human intervention on training data in the development of high-quality LMs.