ErAConD: Error Annotated Conversational Dialog Dataset for Grammatical Error Correction

Currently available grammatical error correction (GEC) datasets are compiled using essays or other long-form text written by language learners, limiting the applicability of these datasets to other domains such as informal writing and conversational dialog. In this paper, we present a novel GEC dataset consisting of parallel original and corrected utterances drawn from open-domain chatbot conversations; this dataset is, to our knowledge, the first GEC dataset targeted to a human-machine conversational setting. We also present a detailed annotation scheme which ranks errors by perceived impact on comprehension, making our dataset more representative of realworld language learning applications. To demonstrate the utility of the dataset, we use our annotated data to fine-tune a state-of-theart GEC model. Experimental results show the effectiveness of our data in improving GEC model performance in a conversational scenario.