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arXiv Computation and Language

A Layered Taxonomy for Chinese Learner Grammatical Error Annotation

The paper introduces a layered taxonomy for annotating grammatical errors in Chinese learner writing, aiming for consistency and linguistic relevance. It first classifies character- and punctuation-level orthographic errors by edit operation and subtype, then assigns other errors a three-layer core label that combines edit operation, linguistic domain, and part of speech, with optional Chinese-specific extensions. The taxonomy is evaluated through coverage analysis of automatically extracted edits and a preliminary consistency study using five large language models, confirming the layered approach while highlighting areas needing refinement.

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From prompting to evidence-based translation: A RAG+prompt system for Japanese-Chinese translation and its pedagogical potential

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arXiv Computation and Language
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Not X, but When: Diagnosing a Mandarin LLM Writing Tic through Contextual Appropriateness

The paper investigates the Mandarin corrective construction "bushi X, ershi Y" (not X, but Y) in large language models, proposing a decision tree that classifies its use as substantive, optional, or defective based on contextual appropriateness. Across five commercial models, the construction appears 2.5 to 9.2 times more frequently than in human text, with 52.8% of annotated instances being optional—correct but unnecessary—while only 1.30% are defective. The study finds that reward models, LLM judges, and fine‑tuning techniques often increase the frequency of this construction, highlighting a gap between correctness and communicative appropriateness in current LLMs.

By Yihang Peng, Yi Jing, Dewen Liu, Weihan Meng, Ying Liu
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