52,370 research outputs found
Using F-structures in machine translation evaluation
Despite a growing interest in automatic evaluation methods for Machine Translation (MT) quality, most existing automatic metrics are still limited to surface comparison of translation and reference strings. In this paper we
show how Lexical-Functional Grammar (LFG) labelled dependencies obtained from an automatic parse can be used to assess the quality of MT on a deeper linguistic level, giving as a result higher correlations with human judgements
Learning labelled dependencies in machine translation evaluation
Recently novel MT evaluation metrics have been presented which go beyond pure string matching, and which correlate
better than other existing metrics with human judgements. Other research in this area has presented machine learning
methods which learn directly from human judgements. In this paper, we present a novel combination of dependency- and
machine learning-based approaches to automatic MT evaluation, and demonstrate greater correlations with human judgement than the existing state-of-the-art methods.
In addition, we examine the extent to which our novel method can be generalised across different tasks and domains
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