2020 AComparativeStudyofSyntheticDat

• (White & Rozovskaya, 2020) ⇒ Max White, and Alla Rozovskaya. (2020). “A Comparative Study of Synthetic Data Generation Methods for Grammatical Error Correction.” In: Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications (BEA@ACL 2020).

Subject Headings: Grammatical Error Correction System.

Cited By

• Google Scholar: ~ 0 Citations.

Quotes

Abstract

Grammatical Error Correction (GEC) is concerned with correcting grammatical errors in written text. Current GEC systems, namely those leveraging statistical and neural machine translation, require large quantities of annotated training data, which can be expensive or impractical to obtain. This research compares techniques for generating synthetic data utilized by the two highest scoring submissions to the restricted and low-resource tracks in the BEA-2019 Shared Task on Grammatical Error Correction.

BibTeX

@inproceedings{2020_AComparativeStudyofSyntheticDat,
  author    = {Max White and
               Alla Rozovskaya},
  editor    = {Jill Burstein and
               Ekaterina Kochmar and
               Claudia Leacock and
               Nitin Madnani and
               Ildiko Pilan and
               Helen Yannakoudakis and
               Torsten Zesch},
  title     = {A Comparative Study of Synthetic Data Generation Methods for Grammatical
               Error Correction},
  booktitle = {Proceedings of the Fifteenth Workshop on Innovative Use of NLP for
               Building Educational Applications (BEA@ACL 2020)},
  pages     = {198--208},
  publisher = {Association for Computational Linguistics},
  year      = {2020},
  url       = {https://doi.org/10.18653/v1/2020.bea-1.21},
  doi       = {10.18653/v1/2020.bea-1.21},
}

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2020 AComparativeStudyofSyntheticDat	Alla Rozovskaya Max White			A Comparative Study of Synthetic Data Generation Methods for Grammatical Error Correction						2020