@inproceedings{mahamood-2024-reprohum,
    title = "{R}epro{H}um {\#}0124-03: Reproducing Human Evaluations of end-to-end approaches for Referring Expression Generation",
    author = "Mahamood, Saad",
    editor = "Balloccu, Simone  and
      Belz, Anya  and
      Huidrom, Rudali  and
      Reiter, Ehud  and
      Sedoc, Joao  and
      Thomson, Craig",
    booktitle = "Proceedings of the Fourth Workshop on Human Evaluation of NLP Systems (HumEval) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.humeval-1.22",
    pages = "250--254",
    abstract = "In this paper we describe our attempt to reproduce a single human evaluation quality criterion of the human evaluation that was in conducted in the paper {``}NeuralREG: An end-to-end approach to referring expression generation{''}. In particular, this paper describes the approach and challenges involved in reproducing the human evaluation as done by the original authors of the paper, the results obtained, and what insights we have gained from attempting this particular reproduction. Insights that we hope will enable refinements to both how human evaluations are documented by author(s) and enable better reproductions of NLP experiments in the future.",
}

In this paper we describe our attempt to reproduce a single human evaluation quality criterion of the human evaluation that was in conducted in the paper {``}NeuralREG: An end-to-end approach to referring expression generation{''}. In particular, this paper describes the approach and challenges involved in reproducing the human evaluation as done by the original authors of the paper, the results obtained, and what insights we have gained from attempting this particular reproduction. Insights that we hope will enable refinements to both how human evaluations are documented by author(s) and enable better reproductions of NLP experiments in the future.
Mahamood, Saad
ReproHum #0124-03: Reproducing Human Evaluations of end-to-end approaches for Referring Expression Generation