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Works14 from public data
- Grammatical Error Correction: A Survey of the State of the Art152
The field is condense into a single article and some of the linguistic challenges of the task are outlined, the most popular datasets that are available to researchers are introduced, and the various methods and techniques that have been developed with a particular focus on artificial error generation are summarized.
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- ALLECS: A Lightweight Language Error Correction System9
ALLECS provides three state-of-the-art base GEC systems using two approaches (sequence-to-sequence generation and sequence tagging), as well as two state- of- the-art GEC system combination methods using two approach (edit-based and text-based).
- Efficient and Interpretable Grammatical Error Correction with Mixture of Experts8
A mixture-of-experts model, MoECE, for grammatical error correction that achieves the performance of T5-XL with three times fewer effective parameters and produces interpretable corrections by also identifying the error type during inference.
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- OpenSeal: Good, Fast, and Cheap Construction of an Open-Source Southeast Asian LLM via Parallel Data3
The findings show that using only parallel data is the most effective way to extend an LLM to new languages, and OpenSeal is built, the first truly open Southeast Asian LLM that rivals the performance of existing models of similar size.
- Just What You Desire: Constrained Timeline Summarization with Self-Reflection for Enhanced Relevance2
- Equity with Efficiency: An Empirical Study of Tokenizers for Multilingual Large Language Models1
The first systematic comparison of equitable tokenizers on a unified benchmark spanning 11 Southeast Asian languages is presented, demonstrating that cross-lingual fairness and tokenization efficiency are not fundamentally at odds, and offer practical guidance for designing equitable multilingual models.
- Anthropogenic Regional Adaptation in Multimodal Vision-Language Model1
The findings establish Anthropogenic Regional Alignment as a foundational paradigm towards applicability of multimodal vision-language models in diverse regions and demonstrate a simple-yet-effective baseline method that optimizes regional value alignment while preserving global generalization.
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- WAMP: Writing, Annotation, and Marking Platform1
This paper proposes a web-based annotation tool – WAMP – that tackles the issue of generating annotated corpora by allowing annotators to annotate essays with ease and export the resulting annotated essays for use in GEC research.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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