Multilingual Grammatical Error Annotation: Combining Language-Agnostic Framework with Language-Specific Flexibility

• URL: http://arxiv.org/abs/2506.07719v1
• Date: Mon, 09 Jun 2025 13:01:19 GMT
• Title: Multilingual Grammatical Error Annotation: Combining Language-Agnostic Framework with Language-Specific Flexibility
• Abstract summary: We introduce a standardized, modular framework for multilingual grammatical error annotation.<n>Our approach combines a language-agnostic foundation with structured language-specific extensions, enabling consistency and flexibility across languages.<n>This work supports scalable and interpretable GEC annotation across languages and promotes more consistent evaluation in multilingual settings.
• Score: 4.524074577209036
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Grammatical Error Correction (GEC) relies on accurate error annotation and evaluation, yet existing frameworks, such as $\texttt{errant}$, face limitations when extended to typologically diverse languages. In this paper, we introduce a standardized, modular framework for multilingual grammatical error annotation. Our approach combines a language-agnostic foundation with structured language-specific extensions, enabling both consistency and flexibility across languages. We reimplement $\texttt{errant}$ using $\texttt{stanza}$ to support broader multilingual coverage, and demonstrate the framework's adaptability through applications to English, German, Czech, Korean, and Chinese, ranging from general-purpose annotation to more customized linguistic refinements. This work supports scalable and interpretable GEC annotation across languages and promotes more consistent evaluation in multilingual settings. The complete codebase and annotation tools can be accessed at https://github.com/open-writing-evaluation/jp_errant_bea.

Related papers

• Tokenizing Crosslingual Homographs [8.254230288283258]
  Multilingual language models rely on shared subword vocabularies to represent multiple languages within a limited number of token units.<n>We investigate this limitation through cross-lingual homographs and false friends.<n>We propose a simple tokenizer-level intervention based on language cues: language-specific characters replacing initial characters of shared-vocabulary words.
  arXiv  Detail & Related papers  (2026-07-20T08:37:31Z)
• A two-stage transliteration approach to improve performance of a multilingual ASR [1.9511556030544333]
  This paper presents an approach to build a language-agnostic end-to-end model trained on a grapheme set. We performed experiments with an end-to-end multilingual speech recognition system for two Indic languages.
  arXiv  Detail & Related papers  (2024-10-09T05:30:33Z)
• LangSAMP: Language-Script Aware Multilingual Pretraining [48.16511046793275]
  We propose Language-Script Aware Multilingual Pretraining (LangSAMP)<n>LangSAMP incorporates both language and script embeddings to enhance representation learning.<n>We apply LangSAMP to the continual pretraining of XLM-R on a highly multilingual corpus covering more than 500 languages.
  arXiv  Detail & Related papers  (2024-09-26T18:29:10Z)
• Understanding and Mitigating Language Confusion in LLMs [76.96033035093204]
  We evaluate 15 typologically diverse languages with existing and newly-created English and multilingual prompts.<n>We find that Llama Instruct and Mistral models exhibit high degrees of language confusion.<n>We find that language confusion can be partially mitigated via few-shot prompting, multilingual SFT and preference tuning.
  arXiv  Detail & Related papers  (2024-06-28T17:03:51Z)
• Breaking the Script Barrier in Multilingual Pre-Trained Language Models with Transliteration-Based Post-Training Alignment [50.27950279695363]
  The transfer performance is often hindered when a low-resource target language is written in a different script than the high-resource source language. Inspired by recent work that uses transliteration to address this problem, our paper proposes a transliteration-based post-pretraining alignment (PPA) method.
  arXiv  Detail & Related papers  (2024-06-28T08:59:24Z)
• Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
  We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
  arXiv  Detail & Related papers  (2023-06-13T08:08:08Z)
• CLSE: Corpus of Linguistically Significant Entities [58.29901964387952]
  We release a Corpus of Linguistically Significant Entities (CLSE) annotated by experts. CLSE covers 74 different semantic types to support various applications from airline ticketing to video games. We create a linguistically representative NLG evaluation benchmark in three languages: French, Marathi, and Russian.
  arXiv  Detail & Related papers  (2022-11-04T12:56:12Z)
• Multilingual BERT has an accent: Evaluating English influences on fluency in multilingual models [23.62852626011989]
  We show that grammatical structures in higher-resource languages bleed into lower-resource languages. We show this bias via a novel method for comparing the fluency of multilingual models to the fluency of monolingual Spanish and Greek models.
  arXiv  Detail & Related papers  (2022-10-11T17:06:38Z)
• Learning to Scale Multilingual Representations for Vision-Language Tasks [51.27839182889422]
  The effectiveness of SMALR is demonstrated with ten diverse languages, over twice the number supported in vision-language tasks to date. We evaluate on multilingual image-sentence retrieval and outperform prior work by 3-4% with less than 1/5th the training parameters compared to other word embedding methods.
  arXiv  Detail & Related papers  (2020-04-09T01:03:44Z)
• On the Importance of Word Order Information in Cross-lingual Sequence Labeling [80.65425412067464]
  Cross-lingual models that fit into the word order of the source language might fail to handle target languages. We investigate whether making models insensitive to the word order of the source language can improve the adaptation performance in target languages.
  arXiv  Detail & Related papers  (2020-01-30T03:35:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.