Papers by Andrew Caines
Grammatical Error Correction for Code-Switched Sentences by Learners of English (2024.lrec-main)
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| Challenge: | Existing grammar error correction systems have been trained on monolingual data and not developed for CSW text. |
| Approach: | They propose a method of generating synthetic CSW GEC datasets by translating different spans of text within existing GEC corpora and investigate different methods of selecting these spans based on CSW ratio, switch-point factor and linguistic constraints. |
| Outcome: | The proposed model achieves an average increase of 1.57 F0.5 across 3 CSW test sets (English-Chinese, English-Korean and English-Japanese) without affecting the model’s performance on a monolingual dataset. |
Prompting open-source and commercial language models for grammatical error correction of English learner text (2024.findings-acl)
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Christopher Davis, Andrew Caines, O Andersen, Shiva Taslimipoor, Helen Yannakoudakis, Zheng Yuan, Christopher Bryant, Marek Rei, Paula Buttery
| Challenge: | Recent advances in generative AI have enabled us to prompt large language models (LLMs) to produce texts which are fluent and grammatical. |
| Approach: | They evaluate model performance by measuring their performance on established benchmarks. |
| Outcome: | The proposed models outperform supervised English GEC models on fluency correction benchmarks and commercial LLMs on edit benchmarks. |
Investigating the effect of auxiliary objectives for the automated grading of learner English speech transcriptions (2020.acl-main)
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| Challenge: | a growing demand for the ability to communicate in English means automated tutoring and assessment systems are becoming more popular. |
| Approach: | They propose to use automatic speech recognition transcripts to grade spontaneous speech based on textual features. |
| Outcome: | The proposed system improves on a transformer encoder with native language identification as an auxiliary task. |
AFRIDOC-MT: Document-level MT Corpus for African Languages (2025.emnlp-main)
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Jesujoba Oluwadara Alabi, Israel Abebe Azime, Miaoran Zhang, Cristina España-Bonet, Rachel Bawden, Dawei Zhu, David Ifeoluwa Adelani, Clement Oyeleke Odoje, Idris Akinade, Iffat Maab, Davis David, Shamsuddeen Hassan Muhammad, Neo Putini, David O. Ademuyiwa, Andrew Caines, Dietrich Klakow
| Challenge: | AFRIDOC-MT is a document-level multi-parallel translation dataset covering five languages . AFRITIC-MT models perform better on sentences than general-purpose LLMs . |
| Approach: | They propose a document-level multi-parallel translation dataset covering English and five African languages. |
| Outcome: | The proposed dataset covers 334 health and 271 information technology news documents . it shows that NLLB-200 achieves the best average performance among standard models . |
Logging Keystrokes in Writing by English Learners (2024.lrec-main)
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Georgios Velentzas, Andrew Caines, Rita Borgo, Erin Pacquetet, Clive Hamilton, Taylor Arnold, Diane Nicholls, Paula Buttery, Thomas Gaillat, Nicolas Ballier, Helen Yannakoudakis
| Challenge: | Essay writing is a skill commonly taught and practised in schools. |
| Approach: | They collect and analyse data representing the essay writing process from start to finish by recording every keystroke from multiple writers participating in the study. |
| Outcome: | The data collected from 1,006 writers is compared against a standard dataset of texts, keystroke logs and metadata for public release. |
Bias Dynamics in BabyLMs: Towards a Compute-Efficient Sandbox for Democratising Pre-Training Debiasing (2026.findings-acl)
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| Challenge: | Pre-trained language models (LMs) have grown substantially in both societal adoption and training costs. |
| Approach: | They propose to use low-cost proxy models to democratise pre-model debiasing research by using small and mutable corpora. |
| Outcome: | The proposed model can approximate bias acquisition and learning dynamics of larger models despite their reduced size. |
Mitigating Frequency Bias and Anisotropy in Language Model Pre-Training with Syntactic Smoothing (2024.emnlp-main)
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| Challenge: | Language models rely on frequency information because they maximize the likelihood of tokens during training. |
| Approach: | They propose a method for quantifying the frequency bias of a language model by assessing sentence-level perplexity with respect to token-level frequency. |
| Outcome: | The proposed method reduces the frequency bias of a language model by inducing a syntactic prior over token representations during pre-training. |
REPROLANG 2020: Automatic Proficiency Scoring of Czech, English, German, Italian, and Spanish Learner Essays (2020.lrec-1)
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| Challenge: | a new paper aims to reproduce the work described in Vajjala & Rama (2018) . the paper focuses on features-based and neural approaches to essay scoring in Czech, German and Italian . |
| Approach: | They propose to replicate the work described in Vajjala & Rama 2018, ‘Experiments with universal CEFR classification’, as part of REPROLANG 2020. |
| Outcome: | The proposed methods perform better than feature-based models for large text datasets, though neural network modifications do bring performance closer to the best feature-driven models. |
PictureStories: Predicting the Task Adherence of Language Learner Answers to a Picture Story-Based Writing Task (2026.eacl-long)
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| Challenge: | a lack of suitable training and evaluation data limits the evaluation of language learning tasks to language proficiency only. |
| Approach: | They develop a marking rubric that covers task adherence with respect to form and content. |
| Outcome: | The proposed model can predict the adherence of learners to written tasks using picture stories. |
Grammatical error detection in transcriptions of spoken English (2020.coling-main)
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| Challenge: | CrowdED corpus of spoken English monologues on business topics was crowdsourced from native speakers of English and learners of English with German as their first language. |
| Approach: | They propose to use the corpus recordings to correct existing speech transcriptions and edit them to make them more fluent. |
| Outcome: | The proposed transcription corrections and annotations can be used for automatic transcription post-editing and grammatical error correction for spoken English. |