Papers by Christopher Bryant
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. |
An Extended Sequence Tagging Vocabulary for Grammatical Error Correction (2023.findings-eacl)
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| Challenge: | Current sequence-to-sequence and sequence-tagging approaches treat GEC as a machine-translation problem. |
| Approach: | They propose to introduce specialised tags for spelling correction and morphological inflection using the SymSpell and LemmInflect algorithms. |
| Outcome: | The proposed approach outperforms existing methods on the BEA benchmark. |
CanVEC - the Canberra Vietnamese-English Code-switching Natural Speech Corpus (2020.lrec-1)
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| Challenge: | Using monolingual tools, code-switching is a problem in the natural language processing community. |
| Approach: | They introduce the Canberra Vietnamese-English Code-switching corpus (CanVEC) which is an original corpus of mixed speech annotated with language information, part of speech tags and Vietnamese translations. |
| Outcome: | The proposed corpus was annotated with language information, part of speech tags and Vietnamese translations using pipelining and monolingual toolkits. |
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. |
Neural Grammatical Error Correction with Finite State Transducers (N19-1)
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| Challenge: | Language model based GEC (LM-GEC) is a promising alternative to SMT and neural sequence-to-sequence models. |
| Approach: | They propose to use finite state transducers to improve LM-GEC by rescoring with neural language models. |
| Outcome: | The proposed model outperforms the best published results on the CoNLL-2014 test set and achieves far better relative improvements over the baselines. |
A Crash Course in Automatic Grammatical Error Correction (2020.coling-tutorials)
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| Challenge: | Grammatical Error Correction (GEC) is the task of automatically detecting and correcting all types of errors in written text. |
| Approach: | tutorial aims to introduce participants to the field of Grammatical Error Correction . aim is to examine the development of neural-based GEC systems . |
| Outcome: | the tutorial aims to introduce participants to the current state of the art in the field of Grammatical Error Correction (GEC) |
How effective is machine translation on low-resource code-switching? A case study comparing human and automatic metrics (2023.findings-acl)
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| Challenge: | Specifically, we compare the performance of three MT systems in terms of their ability to translate monolingual Vietnamese, a low-resource language, and Vietnamese-English CSW respectively. |
| Approach: | They compare the performance of three machine translation systems in the context of machine translation (MT) they find that state-of-the-art neural translation systems achieve higher scores on automatic metrics when processing CSW input . |
| Outcome: | The proposed system can translate monolingual Vietnamese, a low-resource language, and Vietnamese-English CSW respectively. |
Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems (2021.emnlp-main)
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| Challenge: | a multi-class grammatical error detection system can be used to improve grammamatical errors correction (GEC) for English. |
| Approach: | They develop a multi-class grammatical error detection system based on pre-trained ELECTRA and extend it to multi-Class detection using different error type tagsets. |
| Outcome: | The proposed system outperforms previous systems on the BEA-test benchmark. |