Compositional Sequence Labeling Models for Error Detection in Learner Writing
- Submitting institution
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King's College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 142457501
- Type
- E - Conference contribution
- DOI
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10.18653/v1/p16-1112
- Title of conference / published proceedings
- Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- First page
- 1181
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- August
- Year of publication
- 2016
- URL
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https://www.aclweb.org/anthology/P16-1112
- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
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- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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1
- Research group(s)
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-
- Citation count
- 15
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- First deep learning framework for automated Grammatical Error Detection, which remains the state-of-the-art on public datasets (https://www.aclweb.org/anthology/W19-4410.pdf, https://arxiv.org/abs/1906.01154, Wang et al, BEA-2020). The code/models, released on GitHub (https://github.com/marekrei/sequence-labeler; 66 forks), have been successfully adapted for seven different tasks in NLP: error detection in spoken language (Knill et al, ICASSP-2019; Lu et al, INTERSPEECH-2019), lexical simplification (Gooding and Kochmar, EMNLP-2019), part-of-speech tagging (Pratapa et al, EMNLP-2018), named entity recognition (Kurniawan and Louvan, W-NUT-2018; Leonandya and Ikhwantri, CICLing-2019), adversarial training (Tadesse and Collins, ALEC-2018), punctuation prediction (Yi et al, INTERSPEECH-2017), language identification (Khanuja et al, ACL-2020). Resulted in invited presentations UKSpeech2019, ALTE2017.
- Author contribution statement
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- Non-English
- No
- English abstract
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