Wei Li, Xin Zhang, Zhongxin Guo, Shaoguang Mao, Wen Luo, Guangyue Peng, Yangyu Huang, Houfeng Wang, Scarlett Li. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Grammatical error correction (GEC) is a task dedicated to rectifying texts with minimal edits, which can be decoupled into two components: detection and correction.However, previous works have predominantly focused on direct correction, with no prior efforts to integrate both into a single model.Moreover, the exploration of the detection-correction paradigm by large language models (LLMs) remains underdeveloped.This paper introduces an integrated detection-correction structure, named DeCoGLM, based on the General Language Model (GLM).The detection phase employs a fault-tolerant detection template, while the correction phase leverages autoregressive mask infilling for localized error correction.Through the strategic organization of input tokens and modification of attention masks, we facilitate multi-task learning within a single model.Our model demonstrates competitive performance against the state-of-the-art models on English and Chinese GEC datasets.Further experiments present the effectiveness of the detectioncorrection structure in LLMs, suggesting a promising direction for GEC.

Improving robustness against data missing has become one of the core challenges in Multimodal Sentiment Analysis (MSA), which aims to judge speaker sentiments from the language, visual, and acoustic signals. In the current research, translation-based methods and tensor regularization methods are proposed for MSA with incomplete modality features. However, both of them fail to cope with random modality feature missing in non-aligned sequences. In this paper, a transformer-based feature reconstruction network (TFR-Net) is proposed to improve the robustness of models for the random missing in non-aligned modality sequences. First, intra-modal and inter-modal attention-based extractors are adopted to learn robust representations for each element in modality sequences. Then, a reconstruction module is proposed to generate the missing modality features. With the supervision of SmoothL1Loss between generated and complete sequences, TFR-Net is expected to learn semantic-level features corresponding to missing features. Extensive experiments on two public benchmark datasets show that our model achieves good results against data missing across various missing modality combinations and various missing degrees.