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Christopher Bryant

4 accepted papers

2024

Grammatical Error Correction for Code-Switched Sentences by Learners of English

COLING 2024main

Code-switching (CSW) is a common phenomenon among multilingual speakers where multiple languages are used in a single discourse or utterance. Mixed language utterances may still contain grammatical errors however, yet most existing Grammar Error Correction (GEC) systems have been trained on monoling…

2024

Prompting open-source and commercial language models for grammatical error correction of English learner text

ACL 2024findings

Thanks to recent advances in generative AI, we are able to prompt large language models (LLMs) to produce texts which are fluent and grammatical. In addition, it has been shown that we can elicit attempts at grammatical error correction (GEC) from LLMs when prompted with ungrammatical input sentence…

Cited by 20SourcePDFScholar
2023

How effective is machine translation on low-resource code-switching? A case study comparing human and automatic metrics

ACL 2023findings

This paper presents an investigation into the differences between processing monolingual input and code-switching (CSW) input in the context of machine translation (MT). Specifically, we compare the performance of three MT systems (Google, mBART-50 and M2M-100-big) in terms of their ability to trans…

2021

Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems

EMNLP 2021main

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. Specifically, we first develop a new state-of-the-art binary detection system based on pre-trained ELECTRA, and then extend it to multi-class detec…

Cited by 47SourcePDFScholar