Gang Hu, Yating Chen, Haiyan Ding, Wang Gao, Huang Jiajia, Min Peng, Qianqian Xie, Kun Yue. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

With the explosive growth of multimedia content on social media, Multimodal Named Entity Recognition (MNER) has garnered significant attention. However, current paradigms predominantly rely on general Vision-Language Models (VLMs) to generate natural language responses. Such unstructured text generation struggles to precisely articulate the complex structured information inherent in MNER tasks, often resulting in outputs that lack logical rigor and explicit structural constraints. To address these limitations, we propose CodeMNER, a novel framework that reformulates MNER tasks as a multimodal code generation problem. By synthesizing executable code instead of natural language, CodeMNER leverages the inherent syntactic rigor and deterministic executability of programming languages, thereby significantly enhancing the model’s capacity for identifying and classifying named entities. Despite the evident advantages of the code generation paradigm, standard VLMs lack the joint alignment between structured code semantics and natural visual representations, making it challenging to directly establish the mapping from visual contexts to executable code. To this end, we design a progressive four-stage training pipeline, encompassing mid-training, supervised fine-tuning, reinforcement learning with verifiable rewards, and downstream adaptation. This pipeline bridges the inherent vision-code alignment gap and augments model performance on MNER. Extensive experiments across standard Twitter-2015 and Twitter-2017 datasets demonstrate that CodeMNER achieves state-of-the-art performance, surpassing existing baselines.

Large language models (LLMs) excel in general financial tasks and low-resource languages, but their potential in Southeast Asia’ s multilingual financial domain remains underexplored due to cultural diversity, data scarcity, and task complexity. To address this, we introduce SeaFBen, the first open-source benchmark for Southeast Asian multilingual financial tasks. Covering 22k samples across 20 datasets in 5 major languages (Thai, Indonesian, Vietnamese, Filipino, and Malay) from highly populated countries, SeaFBen evaluates 5 key tasks: Knowledge Understanding, Investment Tendency, Credit Rating, Financial Decision-making, and Numerical Reasoning. It pioneers multilingual financial task evaluation, regional localization, and introduces 5 new datasets. Evaluating 12 LLMs reveals significant performance differences, particularly in numerical reasoning, with ChatGPT and SeaLLMs excelling while PolyLM-13B underperforms. Moreover, SeaLLMs’ evaluation reflects language and task performance biases caused by differences in underlying fine-tuning tasks. SeaFBen1is a vital resource for advancing LLM research and applications in financial domain.

Summary Software repositories are crucial resources for many software tasks, including code retrieval and annotation. Programming forums provide questions and answers (Q&A) from software developers, containing abundant code‐description posts for exchanging knowledge about programming issues. However, most posts provide personal opinions of users that are often not adequately confirmed or outdated. Mining software repositories in such open and unrestricted forums is challenging. Since the posts can be arbitrary and noisy, it is difficult to get unified labels for supervised noise elimination. Different from existing mining approaches, this paper proposes Code‐Description Mining Framework (CodeMF), an unsupervised framework to eliminate noisy posts and extract high quality software repositories from programming forums. CodeMF treats all social features of the posts as discrete‐time signals for kernel principal component analysis and further performs wavelet transform feature fusion to find the delicate changes (noises in temporal signals). We conduct comprehensive experiments on StackOverflow. Experimental results demonstrate that CodeMF can effectively reduce running time and improve precision via mining high‐quality software repositories for various programming languages, especially for the large‐scale codebases. To further illustrate the effect of CodeMF applied in software tasks, we introduce it to improve the performance of query‐expansion code search. Meanwhile, for SQL and C# programs, compared to the state‐of‐the‐art query‐expansion method QECK, the improvement of QECK CodeMF is 2% and 6% on Recall@10, and 4% and 14% on mean reciprocal rank, respectively.

Compressing textstreams generated by social networks can both reduce storage consumption and improve efficiency such as fast searching. However, the compression process is a challenge due to the large scale of textstreams. In this article, we propose a textstream compression framework based on compressed sensing theory and design a series of matching parallel procedures. The new approach uses a linear projection technique in the textstream compression process, achieving fast compression speed and low compression ratio. Two processes are executed by designing elaborated parallel procedures for efficient compressing and decompressing of large-scale textstreams. The decompression process is implemented for approximate solutions of underdetermined linear systems. Experimental results show that the new method can efficiently achieve the compression and decompression tasks on a large amount of text generated by social networks.