Non-Autoregressive Grammatical Error Correction Toward a Writing Support System

Hiroki Homma, Mamoru Komachi


Abstract
There are several problems in applying grammatical error correction (GEC) to a writing support system. One of them is the handling of sentences in the middle of the input. Till date, the performance of GEC for incomplete sentences is not well-known. Hence, we analyze the performance of each model for incomplete sentences. Another problem is the correction speed. When the speed is slow, the usability of the system is limited, and the user experience is degraded. Therefore, in this study, we also focus on the non-autoregressive (NAR) model, which is a widely studied fast decoding method. We perform GEC in Japanese with traditional autoregressive and recent NAR models and analyze their accuracy and speed.
Anthology ID:
2020.nlptea-1.1
Volume:
Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications
Month:
December
Year:
2020
Address:
Suzhou, China
Venue:
NLP-TEA
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Publisher:
Association for Computational Linguistics
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Pages:
1–10
Language:
URL:
https://aclanthology.org/2020.nlptea-1.1
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Bibkey:
Cite (ACL):
Hiroki Homma and Mamoru Komachi. 2020. Non-Autoregressive Grammatical Error Correction Toward a Writing Support System. In Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications, pages 1–10, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Non-Autoregressive Grammatical Error Correction Toward a Writing Support System (Homma & Komachi, NLP-TEA 2020)
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