This paper discusses the evaluation of automated metrics developed for the purpose of evaluating machine translation (MT) technology. A general discussion of the usefulness of automated metrics is offered. The NIST MetricsMATR evaluation of MT metrology is described, including its objectives, protocols, participants, and test data. The methodology employed to evaluate the submitted metrics is reviewed. A summary is provided for the general classes of evaluated metrics. Overall results of this evaluation are presented, primarily by means of correlation statistics, showing the degree of agreement between the automated metric scores and the scores of human judgments. Metrics are analyzed at the sentence, document, and system level with results conditioned by various properties of the test data. This paper concludes with some perspective on the improvements that should be incorporated into future evaluations of metrics for MT evaluation.
This article introduces ways in which movement can enhance one’s understanding of how to learn using Experiential Learning Theory (ELT) concepts of the Learning Cycle, Learning Styles, and Learning Flexibility. The theoretical correspondence between the dialectic dimensions of the Learning Cycle and the dimensions of the Laban Movement Analysis (LMA) framework create a hypothesized integrated typology of learning and movement styles that expand the description of Learning Style to include the movement affinities. These suggested relationships are facilitated by the existing theories and grounded by movement observations and interviews of more than 200 adult learners. From the theoretical relationships and observations, the authors propose descriptions of the movement affinities for each of the nine styles in the Kolb Learning Styles Inventory 4.0 (KLSI 4.0) and suggest that increasing one’s movement flexibility, or the ability to move using a full range Effort and motion through space, provides a catalyst for learning and promotes Learning Flexibility. These hypothesized relationships will form the basis for future empirical research.
In this paper we compare two interlingua representations for speech translation. The basis of this paper is a distributional analysis of the C-star II and Nespole databases tagged with interlingua representations. The C-star II database has been partially re-tagged with the Nespole interlingua, which enables us to make comparisons on the same data with two types of interlinguas and on two types of data (Cstar II and Nespole) with the same interlingua. The distributional information presented in this paper show that the Nespole interlingua maintains the language-independence and simplicity of the C-star II speech-actbased approach, while increasing semantic expressiveness and scalability.
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