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arXiv · 1804.11225

Automatic Metric Validation for Grammatical Error Correction

Leshem Choshen·Omri Abend
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Abstract

Metric validation in Grammatical Error Correction (GEC) is currently done by observing the correlation between human and metric-induced rankings. However, such correlation studies are costly, methodologically troublesome, and suffer from low inter-rater agreement. We propose MAEGE, an automatic methodology for GEC metric validation, that overcomes many of the difficulties with existing practices. Experiments with \maege\ shed a new light on metric quality, showing for example that the standard $M^2$ metric fares poorly on corpus-level ranking. Moreover, we use MAEGE to perform a detailed analysis of metric behavior, showing that correcting some types of errors is consistently penalized by existing metrics.

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Leshem Choshen, Omri Abend. 2018-04-30. Automatic Metric Validation for Grammatical Error Correction. https://arxiv.org/abs/1804.11225

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