Enhancing Grammatical Error Correction Systems with Explanations (2023)

Abstract: Grammatical error correction systems improve written communication by detecting and correcting language mistakes. To help language learners better understand why the GEC system makes a certain correction, the causes of errors (evidence words) and the corresponding error types are two key factors. To enhance GEC systems with explanations, we introduce EXPECT, a large dataset annotated with evidence words and grammatical error types. We propose several baselines and anlysis to understand this task. Furthermore, human evaluation verifies our explainable GEC system’s explanations can assist second-language learners in determining whether to accept a correction suggestion and in understanding the associated grammar rule.

## introduction grammatical error correction (gec) systems aim to detect and correct grammatical errors in a given sentence and thus provide useful information for second-language learners. there are two lines of work for building gec systems. sequenceto-sequence methods @xcite @xcite take an erroneous sentence as input and generate an error-free sentence autoregressively. sequence labeling methods @xcite transform the target into a sequence of text-editing actions and use the sequence labeling scheme to predict those actions. with advances in large pre-trained models @xcite and availability of high-quality gec corpora @xcite , academic gec systems @xcite have achieved promising results on benchmarks and serve as strong backbones for modern writing * work was done during the internship at tencent ai lab. ## related work some work formulates gec as a sequence-tosequence problem. among them, transformerbased gec models @xcite have attained state-of-the-art performance on several benchmark datasets @xcite using large plms @xcite and synthetic data @xcite . to avoid the low-efficiency problem of seq2seq decoding, some work @xcite @xcite formats gec as a sequence labeling problem and achieves competitive performance. both lines of work focus on improving the correction performance and decoding speed but cannot provide users with further suggestions. several methods have been proposed to provide explanations for gec systems. errant @xcite designs a rule-based framework as an external function to classify the error type information given a correction. gector @xcite pre-defines g-transformations tag (e.g., transform singular nouns to plurals) and uses a sequence labeling model to predict the tag as explanations directly. example-based gec @xcite adopts the k-nearest-neighbor method @xcite for gec, which can retrieve examples to improve interpretability. despite their success, their explanations are restricted by pre-defined grammar rules or unsupervised retrieval. they may not generalize well to real-life scenarios due to the limited coverage of the widely varying errors made by writers. in contrast, our annotated instances are randomly sampled from real-life human-written corpora without restriction, thus providing a much larger coverage. nagata (2019); nagata et al. (2020); hanawa et al. (2021), and nagata et al. (2021) propose a feedback comment generation task and release two corresponding datasets, which, to our knowledge, are the only two publicly available and large-scale datasets focusing on gec explanations. the task aims to generate a fluent comment describing the erroneous sentence's grammatical error. while this task integrates gec and explainable-gec into one text generation task, we only focus on explainable-gec and formulate it as a labeling task, which is easier and can avoid the high computational cost of seq2seq decoding. furthermore, the evaluation of feedback comment generation mainly relies on human annotators to check if the error types are correctly identified and if the grammatical error correction is proper in the generated text, which is time-consuming and susceptible to the variations resulting from subjective human judgment. in contrast, our token classification task can be easily and fairly evaluated by automatic metrics (e.g., fscore), favoring future research in this direction. ## dataset to facilitate more explainable and instructive grammatical error correction, we propose the expect, an english grammatical error explanation dataset annotated with 15 grammatical error types and corresponding evidence words. ## conclusion we introduce expect, an explainable dataset for grammatical error correction, which contains 21,017 instances with evidence words and error categorization annotation. we implement several models and perform a detailed analysis to understand the dataset better. experiments show that injecting syntactic knowledge can help models to boost their performance. human evaluation verifies the explanations provided by the proposed explainable gec systems are effective in helping second language learners understand the corrections. we hope that expect facilitates future research on building explainable gec systems.