Abstract
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 real-world language learning applications. To demonstrate the utility of the dataset, we use our annotated data to fine-tune a state-of-the-art GEC model. Experimental results show the effectiveness of our data in improving GEC model performance in a conversational scenario.- Anthology ID:
- 2022.naacl-main.5
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 76–84
- Language:
- URL:
- https://aclanthology.org/2022.naacl-main.5
- DOI:
- 10.18653/v1/2022.naacl-main.5
- Cite (ACL):
- Xun Yuan, Derek Pham, Sam Davidson, and Zhou Yu. 2022. ErAConD: Error Annotated Conversational Dialog Dataset for Grammatical Error Correction. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 76–84, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- ErAConD: Error Annotated Conversational Dialog Dataset for Grammatical Error Correction (Yuan et al., NAACL 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.naacl-main.5.pdf
- Code
- yuanxun-yx/eracond
- Data
- ErAConD