Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses
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- Grammatical Error Correction in Low Error Density Domains
Final published version, 344 KB, PDF document
Evaluation of grammatical error correction (GEC) systems has primarily focused on essays written by non-native learners of English, which however is only part of the full spectrum of GEC applications. We aim to broaden the target domain of GEC and release CWEB, a new benchmark for GEC consisting of website text generated by English speakers of varying levels of proficiency. Website data is a common and important domain that contains far fewer grammatical errors than learner essays, which we show presents a challenge to state-of-the-art GEC systems. We demonstrate that a factor behind this is the inability of systems to rely on a strong internal language model in low error density domains. We hope this work shall facilitate the development of open-domain GEC models that generalize to different topics and genres.
Original language | English |
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Title of host publication | Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) |
Publisher | Association for Computational Linguistics |
Publication date | 2020 |
Pages | 8467–8478 |
DOIs | |
Publication status | Published - 2020 |
Event | The 2020 Conference on Empirical Methods in Natural Language Processing - online Duration: 16 Nov 2020 → 20 Nov 2020 http://2020.emnlp.org |
Conference
Conference | The 2020 Conference on Empirical Methods in Natural Language Processing |
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Location | online |
Periode | 16/11/2020 → 20/11/2020 |
Internetadresse |
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