Evaluation of really good grammatical error correction
Robert Östling Katarina Gillholm Murathan Kurfalı Marie Mattson Mats Wirén
Although rarely stated, in practice, Grammatical Error Correction (GEC)
encompasses various models with distinct objectives, ranging from grammatical
error detection to improving fluency. Traditional evaluation methods fail to
fully capture the full range of system capabilities and objectives.
Reference-based evaluations suffer from limitations in capturing the wide
variety of possible correction and the biases introduced during reference
creation and is prone to favor fixing local errors over overall text
improvement. The emergence of large language models (LLMs) has further
highlighted the shortcomings of these evaluation strategies, emphasizing the
need for a paradigm shift in evaluation methodology. In the current study, we
perform a comprehensive evaluation of various GEC systems using a recently
published dataset of Swedish learner texts. The evaluation is performed using
established evaluation metrics as well as human judges. We find that GPT-3 in a
few-shot setting by far outperforms previous grammatical error correction
systems for Swedish, a language comprising only 0.11% of its training data. We
also found that current evaluation methods contain undesirable biases that a
human evaluation is able to reveal. We suggest using human post-editing of GEC
system outputs to analyze the amount of change required to reach native-level
human performance on the task, and provide a dataset annotated with human
post-edits and assessments of grammaticality, fluency and meaning preservation
of GEC system outputs.