Abstract

This dissertation applies nonparametric statistical techniques to Machine Translation (MT) Evaluation using data from a MT Evaluation experiment conducted through a joint Army Research Laboratory (ARL) and Center for the Advanced Study of Language (CASL) project. In particular, the relationship between human task performance on an information extraction task with translated documents and well-known automated translation evaluation metric scores for those documents is studied. Findings from a correlation analysis of the connection between autometrics and task-based metrics are presented and contrasted with current strategies for evaluating translations. A novel idea for assessing partial rank correlation within the presence of grouping factors is also introduced. Lastly, this dissertation presents a framework for task-based machine translation (MT) evaluation and predictive modeling of task responses that gives new information about the relative predic-tive strengths of the different autometrics (and re-coded variants of them) within the statistical Generalized Linear Models developed in analyses of the Information Extraction Task data. This work shows that current autometrics are inadequate with respect to the

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