This report presents a detailed analysis and review of NLP evaluation, in principle and in practice. Part 1 examines evaluation concepts and establishes a framework for NLP system evaluation. This makes use of experience in the related area of information retrieval and the analysis also refers to evaluation in speech processing. Part 2 surveys significant evaluation work done so far, for instance in machine translation, and discusses the particular problems of generic system evaluation. The conclusion is that evaluation strategies and techniques for NLP need much more development, in particular to take proper account of the influence of system tasks and settings. Part 3 develops a general approach to NLP evaluation, aimed at methodologically-sound strategies for test and evaluation motivated by comprehensive performance factor identification. The analysis throughout the report is supported by extensive illustrative examples. This work was carried out under the UK Science and Engineeri...

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This article describes the principles and mechanism of an integrative effort in machine translation (MT) evaluation. Building upon previous standardization initiatives, above all ISO/IEC 9126, 14598 and EAGLES, we attempt to classify into a coherent taxonomy most of the characteristics, attributes and metrics that have been proposed for MT evaluation. The main articulation of this flexible framework is the link between a taxonomy that helps evaluators define a context of use for the evaluated software, and a taxonomy of the quality characteristics and associated metrics. The article explains the theoretical grounds of this articulation, along with an overview of the taxonomies in their present state, and a perspective on ongoing work in MT evaluation standardization.