GLEU: automatic evaluation of sentence-level fluency

In evaluating the output of language technology applications-MT, natural language generation, summarisation-automatic evaluation techniques generally conflate measurement of faithfulness to source content with fluency of the resulting text. In this paper we develop an automatic evaluation metric to estimate fluency alone, by examining the use of parser outputs as metrics, and show that they correlate with human judgements of generated text fluency. We then develop a machine learner based on these, and show that this performs better than the individual parser metrics, approaching a lower bound on human performance. We finally look at different language models for generating sentences, and show that while individual parser metrics can be 'fooled' depending on generation method, the machine learner provides a consistent estimator of fluency.