@inproceedings{bremerman-etal-2020-evaluation,
    title = "On the Evaluation of Machine Translation n-best Lists",
    author = "Bremerman, Jacob  and
      Khayrallah, Huda  and
      Oard, Douglas  and
      Post, Matt",
    editor = "Eger, Steffen  and
      Gao, Yang  and
      Peyrard, Maxime  and
      Zhao, Wei  and
      Hovy, Eduard",
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.eval4nlp-1.7/",
    doi = "10.18653/v1/2020.eval4nlp-1.7",
    pages = "60--68",
    abstract = "The standard machine translation evaluation framework measures the single-best output of machine translation systems. There are, however, many situations where n-best lists are needed, yet there is no established way of evaluating them. This paper establishes a framework for addressing n-best evaluation by outlining three different questions one could consider when determining how one would define a `good' n-best list and proposing evaluation measures for each question. The first and principal contribution is an evaluation measure that characterizes the translation quality of an entire n-best list by asking whether many of the valid translations are placed near the top of the list. The second is a measure that uses gold translations with preference annotations to ask to what degree systems can produce ranked lists in preference order. The third is a measure that rewards partial matches, evaluating the closeness of the many items in an n-best list to a set of many valid references. These three perspectives make clear that having access to many references can be useful when n-best evaluation is the goal."
}