On the Evaluation of Machine Translation n-best Lists

Jacob Bremerman, Huda Khayrallah, Douglas Oard, Matt Post


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.
Anthology ID:
2020.eval4nlp-1.7
Volume:
Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Eval4NLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
60–68
Language:
URL:
https://www.aclweb.org/anthology/2020.eval4nlp-1.7
DOI:
10.18653/v1/2020.eval4nlp-1.7
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.eval4nlp-1.7.pdf