publications

2025

1. FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementation

   Wei Li, Xin Zhang, Zhongxin Guo, and 6 more authors

   In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jul 2025
2. Odysseus Navigates the Sirens’ Song: Dynamic Focus Decoding for Factual and Diverse Open-Ended Text Generation

   Wen Luo, Feifan Song, Wei Li, and 3 more authors

   In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jul 2025
3. Learn to Memorize: Scalable Continual Learning in Semiparametric Models with Mixture-of-Neighbors Induction Memory

   Guangyue Peng, Tao Ge, Wen Luo, and 2 more authors

   In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jul 2025
4. Encode Errors: Representational Retrieval of In-Context Demonstrations for Multilingual Grammatical Error Correction

   Guangyue Peng, Wei Li, Wen Luo, and 1 more author

   In Findings of the Association for Computational Linguistics: ACL 2025, Jul 2025
5. Explanation based In-Context Demonstrations Retrieval for Multilingual Grammatical Error Correction

   Wei Li, Wen Luo, Guangyue Peng, and 1 more author

   In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), Apr 2025

   DOI

2024

1. Detection-Correction Structure via General Language Model for Grammatical Error Correction

   Wei Li and Houfeng Wang

   In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Aug 2024

   DOI

2021

1. Transformer-based Feature Reconstruction Network for Robust Multimodal Sentiment Analysis

   Ziqi* Yuan, Wei* Li, Hua Xu, and 1 more author

   In Proceedings of the 29th ACM International Conference on Multimedia, Virtual Event, China, Aug 2021

   Abs DOI

   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.