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Andrew Caines

7 accepted papers

2025

AFRIDOC-MT: Document-level MT Corpus for African Languages

EMNLP 2025

This paper introduces AFRIDOC-MT, a document-level multi-parallel translation dataset covering English and five African languages: Amharic, Hausa, Swahili, Yorùbá, and Zulu. The dataset comprises 334 health and 271 information technology news documents, all human-translated from English to these lan

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

Logging Keystrokes in Writing by English Learners

COLING 2024main

Essay writing is a skill commonly taught and practised in schools. The ability to write a fluent and persuasive essay is often a major component of formal assessment. In natural language processing and education technology we may work with essays in their final form, for example to carry out automat…

Cited by 5SourcePDFScholar
2024

Mitigating Frequency Bias and Anisotropy in Language Model Pre-Training with Syntactic Smoothing

EMNLP 2024main

Language models strongly rely on frequency information because they maximize the likelihood of tokens during pre-training. As a consequence, language models tend to not generalize well to tokens that are seldom seen during training. Moreover, maximum likelihood training has been discovered to give r…

Cited by 1SourcePDFScholar
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
2020

Grammatical error detection in transcriptions of spoken English

COLING 2020main

We describe the collection of transcription corrections and grammatical error annotations for the CrowdED Corpus of spoken English monologues on business topics. The corpus recordings were crowdsourced from native speakers of English and learners of English with German as their first language. The n…

2019

Automatic Grammatical Error Detection of Non-native Spoken Learner English

ICASSP 2019accepted

Automatic language assessment and learning systems are required to support the global growth in English language learning. They need to be able to provide reliable and meaningful feedback to help learners develop their skills. This paper considers the question of detecting "grammatical" errors in no…

Cited by 0SourceScholar