no code implementations • LREC (MWE) 2022 • Shiva Taslimipoor, Christopher Bryant, Zheng Yuan
Grammatical error correction (GEC) is the task of automatically correcting errors in text.
1 code implementation • EACL (BEA) 2021 • Zheng Yuan, Christopher Bryant
Document-level context can provide valuable information in grammatical error correction (GEC), which is crucial for correcting certain errors and resolving inconsistencies.
1 code implementation • EMNLP 2021 • Zheng Yuan, Shiva Taslimipoor, Christopher Davis, Christopher Bryant
In this paper, we show how a multi-class grammatical error detection (GED) system can be used to improve grammatical error correction (GEC) for English.
1 code implementation • 18 Apr 2024 • Kelvin Wey Han Chan, Christopher Bryant, Li Nguyen, Andrew Caines, Zheng Yuan
Through this exploration, we propose a novel method of generating synthetic CSW GEC datasets by translating different spans of text within existing GEC corpora.
no code implementations • 15 Jan 2024 • Christopher Davis, Andrew Caines, Øistein Andersen, Shiva Taslimipoor, Helen Yannakoudakis, Zheng Yuan, Christopher Bryant, Marek Rei, Paula Buttery
Thanks to recent advances in generative AI, we are able to prompt large language models (LLMs) to produce texts which are fluent and grammatical.
no code implementations • 17 Jul 2023 • Andrew Caines, Luca Benedetto, Shiva Taslimipoor, Christopher Davis, Yuan Gao, Oeistein Andersen, Zheng Yuan, Mark Elliott, Russell Moore, Christopher Bryant, Marek Rei, Helen Yannakoudakis, Andrew Mullooly, Diane Nicholls, Paula Buttery
The recent release of very large language models such as PaLM and GPT-4 has made an unprecedented impact in the popular media and public consciousness, giving rise to a mixture of excitement and fear as to their capabilities and potential uses, and shining a light on natural language processing research which had not previously received so much attention.
2 code implementations • 12 Feb 2023 • Stuart Mesham, Christopher Bryant, Marek Rei, Zheng Yuan
We extend a current sequence-tagging approach to Grammatical Error Correction (GEC) by introducing specialised tags for spelling correction and morphological inflection using the SymSpell and LemmInflect algorithms.
no code implementations • 9 Nov 2022 • Christopher Bryant, Zheng Yuan, Muhammad Reza Qorib, Hannan Cao, Hwee Tou Ng, Ted Briscoe
Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text.
1 code implementation • 28 Oct 2022 • Christopher Davis, Christopher Bryant, Andrew Caines, Marek Rei, Paula Buttery
Targeted studies testing knowledge of subject-verb agreement (SVA) indicate that pre-trained language models encode syntactic information.
1 code implementation • 7 Oct 2021 • Dan Hirlea, Christopher Bryant, Maurizio Zollo, Marek Rei
We introduce the novel task of detecting sustainability initiatives in company reports.
no code implementations • COLING 2020 • Roman Grundkiewicz, Christopher Bryant, Mariano Felice
Grammatical Error Correction (GEC) is the task of automatically detecting and correcting all types of errors in written text.
no code implementations • LREC 2020 • Li Nguyen, Christopher Bryant
This paper introduces the Canberra Vietnamese-English Code-switching corpus (CanVEC), an original corpus of natural mixed speech that we semi-automatically annotated with language information, part of speech (POS) tags and Vietnamese translations.
no code implementations • WS 2019 • Christopher Bryant, Mariano Felice, {\O}istein E. Andersen, Ted Briscoe
This paper reports on the BEA-2019 Shared Task on Grammatical Error Correction (GEC).
no code implementations • NAACL 2019 • Felix Stahlberg, Christopher Bryant, Bill Byrne
Language model based GEC (LM-GEC) is a promising alternative which does not rely on annotated training data.
no code implementations • WS 2018 • Christopher Bryant, Ted Briscoe
Since the end of the CoNLL-2014 shared task on grammatical error correction (GEC), research into language model (LM) based approaches to GEC has largely stagnated.
1 code implementation • ACL 2017 • Christopher Bryant, Mariano Felice, Ted Briscoe
Until now, error type performance for Grammatical Error Correction (GEC) systems could only be measured in terms of recall because system output is not annotated.
no code implementations • COLING 2016 • Mariano Felice, Christopher Bryant, Ted Briscoe
We propose a new method of automatically extracting learner errors from parallel English as a Second Language (ESL) sentences in an effort to regularise annotation formats and reduce inconsistencies.