Is this the end of the gold standard? A straightforward reference-less grammatical error correction metric

Abstract

It is difficult to rank and evaluate the performance of grammatical error correction (GEC) systems, as a sentence can be rewritten in numerous correct ways. A number of GEC metrics have been used to evaluate proposed GEC systems; however, each system relies on either a comparison with one or more reference texts-in what is known as the gold standard for reference-based metrics-or a separate annotated dataset to fine-tune the reference-less metric. Reference-based systems have a low correlation with human judgement, cannot capture all the ways in which a sentence can be corrected, and require substantial work to develop a test dataset. We propose a referenceless GEC evaluation system that is strongly correlated with human judgement, solves the issues related to the use of a reference, and does not need another annotated dataset for fine-tuning. The proposed system relies solely on commonly available tools. Additionally, currently available reference-less metrics do not work properly when part of a sentence is repeated as opposed to reference-based metrics. In our proposed system, we look to address issues inherent in reference-less metrics and reference-based metrics.

Grammatical Error Correction: A Survey of the State of the Art

“…Scribendi Score. The Scribendi Score (Islam and Magnani 2021) was designed to be simpler than other reference-less metrics in that it requires neither an existing GEC system nor fine-tuning. Instead, it calculates an absolute score (1=positive, -1=negative, 0=no change) from a combination of language model perplexity (GPT2: Radford et al (2019)) and sorted token/Levenshtein distance ratios, which respectively ensure that i) the corrected sentence is more probable than the original and ii) both sentences are not significantly different from each other.…”