We present a reproduction study of the human evaluation of the coverage of fact checking explanations conducted by Atanasova et al. (2020), as a team in Track B of ReproNLP 2024. The setup of our reproduction study is almost the same as the original study, with some necessary modifications to the evaluation guideline and annotation interface. Our reproduction achieves a higher IAA of 0.20 compared to the original study’s 0.12, but discovers a mismatch between the IAA calculated by us with the raw annotation in the original study and the IAA reported in the original paper. Additionally, our reproduction results on the ranks of three types of explanations are drastically different from the original experiment, rendering that one important conclusion in the original paper cannot be confirmed at all. The case study illustrates that the annotators in the reproduction study may understand the quality criterion differently from the annotators in the original study.

This paper reports a reproduction study of the human evaluation of role-oriented dialogue summarization models, as part of the ReproNLP Shared Task 2023 on Reproducibility of Evaluations in NLP. We outline the disparities between the original study’s experimental design and our reproduction study, along with the outcomes obtained. The inter-annotator agreement within the reproduction study is observed to be lower, measuring 0.40 as compared to the original study’s 0.48. Among the six conclusions drawn in the original study, four are validated in our reproduction study. We confirm the effectiveness of the proposed approach on the overall metric, albeit with slightly poorer relative performance compared to the original study. Furthermore, we raise an open-ended inquiry: how can subjective practices in the original study be identified and addressed when conducting reproduction studies?

We report our efforts in identifying a set of previous human evaluations in NLP that would be suitable for a coordinated study examining what makes human evaluations in NLP more/less reproducible. We present our results and findings, which include that just 13% of papers had (i) sufficiently low barriers to reproduction, and (ii) enough obtainable information, to be considered for reproduction, and that all but one of the experiments we selected for reproduction was discovered to have flaws that made the meaningfulness of conducting a reproduction questionable. As a result, we had to change our coordinated study design from a reproduce approach to a standardise-then-reproduce-twice approach. Our overall (negative) finding that the great majority of human evaluations in NLP is not repeatable and/or not reproducible and/or too flawed to justify reproduction, paints a dire picture, but presents an opportunity for a rethink about how to design and report human evaluations in NLP.