Xin Su

Jingheng Ye, Zishan Xu, Yinghui Li et al.

The paper focuses on the interpretability of Grammatical Error Correction (GEC) evaluation metrics, which received little attention in previous studies. To bridge the gap, we introduce **CLEME2.0**, a reference-based metric describing four fundamental aspects of GEC systems: hit-correction, wrong-correction, under-correction, and over-correction. They collectively contribute to exposing critical qualities and locating drawbacks of GEC systems. Evaluating systems by combining these aspects also leads to superior human consistency over other reference-based and reference-less metrics. Extensive experiments on two human judgment datasets and six reference datasets demonstrate the effectiveness and robustness of our method, achieving a new state-of-the-art result. Our codes are released at https://github.com/THUKElab/CLEME.

Sisi Dai, Xinxin Su, Boyan Wan et al.

Recent advancements in diffusion generative models significantly advanced image, video, and 3D content creation from user-provided text prompts. However, the challenging problem of dynamic 3D content generation (text-to-4D) with diffusion guidance remains largely unexplored. In this paper, we introduce TextMesh4D, a novel framework for high-quality text-to-4D generation. Our approach leverages per-face Jacobians as a differentiable mesh representation and decomposes 4D generation into two stages: static object creation and dynamic motion synthesis. We further propose a flexibility-rigidity regularization term to stabilize Jacobian optimization under video diffusion priors, ensuring robust geometric performance. Experiments demonstrate that TextMesh4D achieves state-of-the-art results in terms of temporal consistency, structural fidelity, and visual realism. Moreover, TextMesh4D operates with a low GPU memory overhead-requiring only a single 24GB GPU-offering a cost-effective yet high-quality solution for text-driven 4D mesh generation. The code will be released to facilitate future research in text-to-4D generation.

Ding Zhang, Yangning Li, Lichen Bai et al.

Chinese grammatical error correction (CGEC) aims to detect and correct errors in the input Chinese sentences. Recently, Pre-trained Language Models (PLMS) have been employed to improve the performance. However, current approaches ignore that correction difficulty varies across different instances and treat these samples equally, enhancing the challenge of model learning. To address this problem, we propose a multi-granularity Curriculum Learning (CL) framework. Specifically, we first calculate the correction difficulty of these samples and feed them into the model from easy to hard batch by batch. Then Instance-Level CL is employed to help the model optimize in the appropriate direction automatically by regulating the loss function. Extensive experimental results and comprehensive analyses of various datasets prove the effectiveness of our method.