arXiv:2606. 00020v1 Announce Type: cross Abstract: Large Language Model (LLM) based Chinese Grammatical Error Correction (CGEC) systems face two critical challenges: general-purpose models lack specialized linguistic priors for subtle grammatical distinctions, and Supervised Fine-Tuning (SFT) with Maximum Likelihood Estimation fails to optimize for precision-focused metrics, leading to systematic over-correction.
By Wei Tian, Yuhao Zhou, Man Lan
arXiv:2608.28614v1 Announce Type: cross
Abstract: Sequence-to-edit approaches make grammatical error correction (GEC) efficient and locally interpretable by predicting edit labels over the input rath...
By Wenjie Lou, Alaa Mamdouh Akef
arXiv:2608.09289v2 Announce Type: replace
Abstract: Second language writing research distinguishes grammatical accuracy from native-like idiomaticity, yet automated writing evaluation often conflates...
By Steve Woollaston, Brendan Flanagan, Hiroaki Ogata
arXiv:2605. 20712v2 Announce Type: replace-cross Abstract: Automatic speech recognition replaces typing only when correction costs less than manual entry - a threshold determined by error types, not counts: fixing a misrecognized domain term costs far more than inserting a comma.
By Kavya Manohar, Arghya Bhattacharya, Kush Juvekar, Kumarmanas Nethil
arXiv:2601. 16217v2 Announce Type: replace-cross Abstract: Large language models increasingly mediate multilingual professional communication, where useful generation requires adapting to community conventions about which expressions are retained, translated, or mixed.
By Qingyan Yang, Tongxi Wang, Yunsheng Luo
Fine-grained morphosyntactic error annotation is important in clinical and developmental language research, yet it is labour-intensive, expert-dependent, and difficult to scale. We present TalkTag, an LLM-based lightweight tool fine-tuned to automate CHAT-style error annotation in spoken-language transcripts.
Bangla-English tutoring requires more than producing a correct translation: learners also need explanations of grammar differences, awareness of their likely errors, and targeted practice. We present...
arXiv:2608. 08283v1 Announce Type: cross Abstract: Although large language models can translate some historical languages surprisingly well, their usefulness in digital humanities workflows is limited by the lack of reliable evaluation.
By Osvaldo Quinjica, Eric Bennett, Xinchen Yang, Andrew Schonebaum, Marine Carpuat
The paper introduces GUIDE, a generative unsupervised framework for Chinese query correction that uses shared-ID encoding for phonetically or visually confusable characters and an encoder–decoder architecture to reconstruct queries within plausible confusion neighborhoods. It incorporates a time‑decayed, query‑frequency‑weighted objective to adapt to rapidly changing query vocabularies. Experiments on QSpell 250K and the large‑scale KwaiSearch dataset demonstrate that GUIDE consistently outperforms strong baselines, with online A/B testing confirming improvements in correction quality and downstream engagement.
By Lei Yang, Binbin Huang, Jiwei Tan, Xuhui Sui, Chang Tu, Yi Wang, Han Li
The paper presents an end‑to‑end sequence‑to‑sequence approach for correcting Tamil spelling and grammar errors, leveraging progressively fine‑tuned transformer models (mT5‑small and mBART‑50). Using a synthetic corpus of 657,720 noisy‑clean sentence pairs across ten error categories, the authors introduce a four‑stage training schedule that targets surface noise, contextual grammar, single‑site sandhi, and multi‑site cross‑word sandhi. The best model, mBART‑50 v5, achieves 69.3% exact‑match accuracy on a balanced diagnostic set, with notable gains in sandhi (87.5%) and subject‑verb agreement (43.5%) accuracy, while also revealing a precision‑recall trade‑off for sandhi corrections.
By Karthikeyan A, Jaya Nirmala S, Sangeetha Sivanesan, Indhu R, Pranav Kumar, Bharat Jude Johnson, Vishnu Ram
arXiv:2606. 02837v1 Announce Type: cross Abstract: Accurate translation from Natural Language to First-Order Logic (NL-to-FOL) underpins neurosymbolic AI systems and Natural Language Inference (NLI), making the quality of NL-to-FOL benchmarks essential -- yet these datasets have never been rigorously audited.
By Andrea Brunello, Cristian Curaba, Luca Geatti, Michele Mignani, Angelo Montanari, Nicola Saccomanno
arXiv:2607. 11276v1 Announce Type: cross Abstract: Ensuring the quality of educational materials requires more than standard proofreading: textbooks must be audited for factual accuracy, domain-specific technical correctness, and linguistic quality simultaneously -- a task that general-purpose grammar checkers cannot address.
By Ciprian Cristescu, Adrian-Marius Dumitran, Angela-Liliana Dumitran, Gabriel Stefan