Christopher Bryant


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An Extended Sequence Tagging Vocabulary for Grammatical Error Correction
Stuart Mesham | Christopher Bryant | Marek Rei | Zheng Yuan
Findings of the Association for Computational Linguistics: EACL 2023

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. Our approach improves generalisation: the proposed new tagset allows a smaller number of tags to correct a larger range of errors. Our results show a performance improvement both overall and in the targeted error categories. We further show that ensembles trained with our new tagset outperform those trained with the baseline tagset on the public BEA benchmark.

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How effective is machine translation on low-resource code-switching? A case study comparing human and automatic metrics
Li Nguyen | Christopher Bryant | Oliver Mayeux | Zheng Yuan
Findings of the Association for Computational Linguistics: ACL 2023

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 translate monolingual Vietnamese, a low-resource language, and Vietnamese-English CSW respectively. To our knowledge, this is the first study to systematically analyse what might happen when multilingual MT systems are exposed to CSW data using both automatic and human metrics. We find that state-of-the-art neural translation systems not only achieve higher scores on automatic metrics when processing CSW input (compared to monolingual input), but also produce translations that are consistently rated as more semantically faithful by humans. We further suggest that automatic evaluation alone is insufficient for evaluating the translation of CSW input. Our findings establish a new benchmark that offers insights into the relationship between MT and CSW.

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MultiGED-2023 shared task at NLP4CALL: Multilingual Grammatical Error Detection
Elena Volodina | Christopher Bryant | Andrew Caines | Orphée De Clercq | Jennifer-Carmen Frey | Elizaveta Ershova | Alexandr Rosen | Olga Vinogradova
Proceedings of the 12th Workshop on NLP for Computer Assisted Language Learning


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Improving Grammatical Error Correction for Multiword Expressions
Shiva Taslimipoor | Christopher Bryant | Zheng Yuan
Proceedings of the 18th Workshop on Multiword Expressions @LREC2022

Grammatical error correction (GEC) is the task of automatically correcting errors in text. It has mainly been developed to assist language learning, but can also be applied to native text. This paper reports on preliminary work in improving GEC for multiword expression (MWE) error correction. We propose two systems which incorporate MWE information in two different ways: one is a multi-encoder decoder system which encodes MWE tags in a second encoder, and the other is a BART pre-trained transformer-based system that encodes MWE representations using special tokens. We show improvements in correcting specific types of verbal MWEs based on a modified version of a standard GEC evaluation approach.

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Probing for targeted syntactic knowledge through grammatical error detection
Christopher Davis | Christopher Bryant | Andrew Caines | Marek Rei | Paula Buttery
Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)

Targeted studies testing knowledge of subject-verb agreement (SVA) indicate that pre-trained language models encode syntactic information. We assert that if models robustly encode subject-verb agreement, they should be able to identify when agreement is correct and when it is incorrect. To that end, we propose grammatical error detection as a diagnostic probe to evaluate token-level contextual representations for their knowledge of SVA. We evaluate contextual representations at each layer from five pre-trained English language models: BERT, XLNet, GPT-2, RoBERTa and ELECTRA. We leverage public annotated training data from both English second language learners and Wikipedia edits, and report results on manually crafted stimuli for subject-verb agreement. We find that masked language models linearly encode information relevant to the detection of SVA errors, while the autoregressive models perform on par with our baseline. However, we also observe a divergence in performance when probes are trained on different training sets, and when they are evaluated on different syntactic constructions, suggesting the information pertaining to SVA error detection is not robustly encoded.


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Document-level grammatical error correction
Zheng Yuan | Christopher Bryant
Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications

Document-level context can provide valuable information in grammatical error correction (GEC), which is crucial for correcting certain errors and resolving inconsistencies. In this paper, we investigate context-aware approaches and propose document-level GEC systems. Additionally, we employ a three-step training strategy to benefit from both sentence-level and document-level data. Our system outperforms previous document-level and all other NMT-based single-model systems, achieving state of the art on a common test set.

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Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems
Zheng Yuan | Shiva Taslimipoor | Christopher Davis | Christopher Bryant
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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 detection using different error type tagsets derived from the ERRANT framework. Output from this detection system is used as auxiliary input to fine-tune a novel encoder-decoder GEC model, and we subsequently re-rank the N-best GEC output to find the hypothesis that most agrees with the GED output. Results show that fine-tuning the GEC system using 4-class GED produces the best model, but re-ranking using 55-class GED leads to the best performance overall. This suggests that different multi-class GED systems benefit GEC in different ways. Ultimately, our system outperforms all other previous work that combines GED and GEC, and achieves a new single-model NMT-based state of the art on the BEA-test benchmark.


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CanVEC - the Canberra Vietnamese-English Code-switching Natural Speech Corpus
Li Nguyen | Christopher Bryant
Proceedings of the Twelfth Language Resources and Evaluation Conference

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 corpus, which was built to inform a sociolinguistic study on language variation and code-switching, consists of 10 hours of recorded speech (87k tokens) between 45 Vietnamese-English bilinguals living in Canberra, Australia. We describe how we collected and annotated the corpus by pipelining several monolingual toolkits to considerably speed up the annotation process. We also describe how we evaluated the automatic annotations to ensure corpus reliability. We make the corpus available for research purposes.

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A Crash Course in Automatic Grammatical Error Correction
Roman Grundkiewicz | Christopher Bryant | Mariano Felice
Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts

Grammatical Error Correction (GEC) is the task of automatically detecting and correcting all types of errors in written text. Although most research has focused on correcting errors in the context of English as a Second Language (ESL), GEC can also be applied to other languages and native text. The main application of a GEC system is thus to assist humans with their writing. Academic and commercial interest in GEC has grown significantly since the Helping Our Own (HOO) and Conference on Natural Language Learning (CoNLL) shared tasks in 2011-14, and a record-breaking 24 teams took part in the recent Building Educational Applications (BEA) shared task. Given this interest, and the recent shift towards neural approaches, we believe the time is right to offer a tutorial on GEC for researchers who may be new to the field or who are interested in the current state of the art and future challenges. With this in mind, the main goal of this tutorial is not only to bring attendees up to speed with GEC in general, but also examine the development of neural-based GEC systems.


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The BEA-2019 Shared Task on Grammatical Error Correction
Christopher Bryant | Mariano Felice | Øistein E. Andersen | Ted Briscoe
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

This paper reports on the BEA-2019 Shared Task on Grammatical Error Correction (GEC). As with the CoNLL-2014 shared task, participants are required to correct all types of errors in test data. One of the main contributions of the BEA-2019 shared task is the introduction of a new dataset, the Write&Improve+LOCNESS corpus, which represents a wider range of native and learner English levels and abilities. Another contribution is the introduction of tracks, which control the amount of annotated data available to participants. Systems are evaluated in terms of ERRANT F_0.5, which allows us to report a much wider range of performance statistics. The competition was hosted on Codalab and remains open for further submissions on the blind test set.

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Neural Grammatical Error Correction with Finite State Transducers
Felix Stahlberg | Christopher Bryant | Bill Byrne
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Grammatical error correction (GEC) is one of the areas in natural language processing in which purely neural models have not yet superseded more traditional symbolic models. Hybrid systems combining phrase-based statistical machine translation (SMT) and neural sequence models are currently among the most effective approaches to GEC. However, both SMT and neural sequence-to-sequence models require large amounts of annotated data. Language model based GEC (LM-GEC) is a promising alternative which does not rely on annotated training data. We show how to improve LM-GEC by applying modelling techniques based on finite state transducers. We report further gains by rescoring with neural language models. We show that our methods developed for LM-GEC can also be used with SMT systems if annotated training data is available. Our best system outperforms the best published result on the CoNLL-2014 test set, and achieves far better relative improvements over the SMT baselines than previous hybrid systems.


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Language Model Based Grammatical Error Correction without Annotated Training Data
Christopher Bryant | Ted Briscoe
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications

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. In this paper, we re-examine LMs in GEC and show that it is entirely possible to build a simple system that not only requires minimal annotated data (∼1000 sentences), but is also fairly competitive with several state-of-the-art systems. This approach should be of particular interest for languages where very little annotated training data exists, although we also hope to use it as a baseline to motivate future research.


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Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction
Christopher Bryant | Mariano Felice | Ted Briscoe
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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. To overcome this problem, we introduce ERRANT, a grammatical ERRor ANnotation Toolkit designed to automatically extract edits from parallel original and corrected sentences and classify them according to a new, dataset-agnostic, rule-based framework. This not only facilitates error type evaluation at different levels of granularity, but can also be used to reduce annotator workload and standardise existing GEC datasets. Human experts rated the automatic edits as “Good” or “Acceptable” in at least 95% of cases, so we applied ERRANT to the system output of the CoNLL-2014 shared task to carry out a detailed error type analysis for the first time.


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Automatic Extraction of Learner Errors in ESL Sentences Using Linguistically Enhanced Alignments
Mariano Felice | Christopher Bryant | Ted Briscoe
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

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. Specifically, given an original and corrected sentence, our method first uses a linguistically enhanced alignment algorithm to determine the most likely mappings between tokens, and secondly employs a rule-based function to decide which alignments should be merged. Our method beats all previous approaches on the tested datasets, achieving state-of-the-art results for automatic error extraction.


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The CoNLL-2015 Shared Task on Shallow Discourse Parsing
Nianwen Xue | Hwee Tou Ng | Sameer Pradhan | Rashmi Prasad | Christopher Bryant | Attapol Rutherford
Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task

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How Far are We from Fully Automatic High Quality Grammatical Error Correction?
Christopher Bryant | Hwee Tou Ng
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)


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Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task
Hwee Tou Ng | Siew Mei Wu | Ted Briscoe | Christian Hadiwinoto | Raymond Hendy Susanto | Christopher Bryant
Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task

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The CoNLL-2014 Shared Task on Grammatical Error Correction
Hwee Tou Ng | Siew Mei Wu | Ted Briscoe | Christian Hadiwinoto | Raymond Hendy Susanto | Christopher Bryant
Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task