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Found 20 papers, 8 papers with code
Improving Grammatical Error Correction for Multiword Expressions
Grammatical error correction (GEC) is the task of automatically correcting errors in text.
Document-level grammatical error correction
Document-level context can provide valuable information in grammatical error correction (GEC), which is crucial for correcting certain errors and resolving inconsistencies.
Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems
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.
Grammatical Error Correction for Code-Switched Sentences by Learners of English
Through this exploration, we propose a novel method of generating synthetic CSW GEC datasets by translating different spans of text within existing GEC corpora.
Prompting open-source and commercial language models for grammatical error correction of English learner text
Thanks to recent advances in generative AI, we are able to prompt large language models (LLMs) to produce texts which are fluent and grammatical.
On the application of Large Language Models for language teaching and assessment technology
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.
An Extended Sequence Tagging Vocabulary for Grammatical Error Correction
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.
Grammatical Error Correction: A Survey of the State of the Art
Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text.
Probing for targeted syntactic knowledge through grammatical error detection
Targeted studies testing knowledge of subject-verb agreement (SVA) indicate that pre-trained language models encode syntactic information.
Contextual Sentence Classification: Detecting Sustainability Initiatives in Company Reports
We introduce the novel task of detecting sustainability initiatives in company reports.
A Crash Course in Automatic Grammatical Error Correction
Grammatical Error Correction (GEC) is the task of automatically detecting and correcting all types of errors in written text.
CanVEC - the Canberra Vietnamese-English Code-switching Natural Speech Corpus
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.
The BEA-2019 Shared Task on Grammatical Error Correction
This paper reports on the BEA-2019 Shared Task on Grammatical Error Correction (GEC).
Neural Grammatical Error Correction with Finite State Transducers
Language model based GEC (LM-GEC) is a promising alternative which does not rely on annotated training data.
Language Model Based Grammatical Error Correction without Annotated Training Data
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.
Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction
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.
Automatic Extraction of Learner Errors in ESL Sentences Using Linguistically Enhanced Alignments
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.
