Kathryn B. Taylor


Task-based evaluation for machine translation
Jennifer B. Doyon | Kathryn B. Taylor | John S. White
Proceedings of Machine Translation Summit VII

In an effort to reduce the subjectivity, cost, and complexity of evaluation methods for machine translation (MT) and other language technologies, task-based assessment is examined as an alternative to metrics-based in human judgments about MT, i.e., the previously applied adequacy, fluency, and informativeness measures. For task-based evaluation strategies to be employed effectively to evaluate languageprocessing technologies in general, certain key elements must be known. Most importantly, the objectives the technology’s use is expected to accomplish must be known, the objectives must be expressed as tasks that accomplish the objectives, and then successful outcomes defined for the tasks. For MT, task-based evaluation is correlated to a scale of tasks, and has as its premise that certain tasks are more forgiving of errors than others. In other words, a poor translation may suffice to determine the general topic of a text, but may not permit accurate identification of participants or the specific event. The ordering of tasks according to their tolerance for errors, as determined by actual task outcomes provided in this paper, is the basis of a scale and repeatable process by which to measure MT systems that has advantages over previous methods.