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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2112.08466 |
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| _version_ | 1866918130296553472 |
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| author | Yuan, Xun Pham, Derek Davidson, Sam Yu, Zhou |
| author_facet | Yuan, Xun Pham, Derek Davidson, Sam Yu, Zhou |
| contents | Currently available grammatical error correction (GEC) datasets are compiled using well-formed written text, limiting the applicability of these datasets to other domains such as informal writing and dialog. In this paper, we present a novel parallel GEC dataset drawn from open-domain chatbot conversations; this dataset is, to our knowledge, the first GEC dataset targeted to a conversational setting. To demonstrate the utility of the dataset, we use our annotated data to fine-tune a state-of-the-art GEC model, resulting in a 16 point increase in model precision. This is of particular importance in a GEC model, as model precision is considered more important than recall in GEC tasks since false positives could lead to serious confusion in language learners. We also present a detailed annotation scheme which ranks errors by perceived impact on comprehensibility, making our dataset both reproducible and extensible. Experimental results show the effectiveness of our data in improving GEC model performance in conversational scenario. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2112_08466 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | ErAConD : Error Annotated Conversational Dialog Dataset for Grammatical Error Correction Yuan, Xun Pham, Derek Davidson, Sam Yu, Zhou Computation and Language Currently available grammatical error correction (GEC) datasets are compiled using well-formed written text, limiting the applicability of these datasets to other domains such as informal writing and dialog. In this paper, we present a novel parallel GEC dataset drawn from open-domain chatbot conversations; this dataset is, to our knowledge, the first GEC dataset targeted to a conversational setting. To demonstrate the utility of the dataset, we use our annotated data to fine-tune a state-of-the-art GEC model, resulting in a 16 point increase in model precision. This is of particular importance in a GEC model, as model precision is considered more important than recall in GEC tasks since false positives could lead to serious confusion in language learners. We also present a detailed annotation scheme which ranks errors by perceived impact on comprehensibility, making our dataset both reproducible and extensible. Experimental results show the effectiveness of our data in improving GEC model performance in conversational scenario. |
| title | ErAConD : Error Annotated Conversational Dialog Dataset for Grammatical Error Correction |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2112.08466 |