Xianda Zheng, Huan Gao, Meng-Fen Chiang, Michael J. Witbrock, Kaiqi Zhao, Shangyang Li. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Xianda Zheng, Zijian Huang, Meng-Fen Chiang, Jiamou Liu, Yuan Fang, Michael J. Witbrock, Kaiqi Zhao. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Learnersourcing offers great potential for scalable education through student content creation. However, predicting student performance on learnersourced questions, which is essential for personalizing the learning experience, is challenging due to the inherent noise in student-generated data. Moreover, while conventional graph-based methods can capture the complex network of student and question interactions, they often fall short under cold start conditions where limited student engagement with questions yields sparse data. To address both challenges, we introduce an innovative strategy that synergizes the potential of integrating Signed Graph Neural Networks (SGNNs) and Large Language Model (LLM) embeddings. Our methodology employs a signed bipartite graph to comprehensively model student answers, complemented by a contrastive learning framework that enhances noise resilience. Furthermore, LLM's contribution lies in generating foundational question embeddings, proving especially advantageous in addressing cold start scenarios characterized by limited graph data. Validation across five real-world datasets sourced from the PeerWise platform underscores our approach's effectiveness. Our method outperforms baselines, showcasing enhanced predictive accuracy and robustness.

Judgment summaries are beneficial for legal practitioners to comprehend and retrieve case law efficiently. Unlike summaries in general domains, e.g., news, judgment summaries often require a clear structure. Such a structure helps readers grasp the information contained in the summary and reduces information loss. To the best of our knowledge, none of the existing text summarizers can generate summaries aligned with the summary structure in the legal domain. Inspired by this observation, this paper introduces a Summary Structure-Enhanced (SSE) method to synthesize structured summaries for legal documents. SSE can easily be incorporated into the Encoder-Decoder framework, which is commonly adopted in state-of-the-art text summarizers. Experiments on the datasets of New Zealand and Chinese judgments show that the proposed method consistently improves the performance of state-of-the-art summarizers in terms of Rouge scores.

This paper is the first to use contrastive learning to improve the robustness of graph representation learning for signed bipartite graphs, which are commonly found in social networks, recommender systems, and paper review platforms. Existing contrastive learning methods for signed graphs cannot capture implicit relations between nodes of the same type in signed bipartite graphs, which have two types of nodes and edges only connect nodes of different types. We propose a Signed Bipartite Graph Contrastive Learning (SBGCL) method to learn robust node representation while retaining the implicit relations between nodes of the same type. SBGCL augments a signed bipartite graph with a novel two-level graph augmentation method. At the top level, we maintain two perspectives of the signed bipartite graph, one presents the original interactions between nodes of different types, and the other presents the implicit relations between nodes of the same type. At the bottom level, we employ stochastic perturbation strategies to create two perturbed graphs in each perspective. Then, we construct positive and negative samples from the perturbed graphs and design a multi-perspective contrastive loss to unify the node presentations learned from the two perspectives. Results show proposed model is effective over state-of-the-art methods on real-world datasets.

Signed graphs model complex relations using both positive and negative edges. Signed graph neural networks (SGNN) are powerful tools to analyze signed graphs. We address the vulnerability of SGNN to potential edge noise in the input graph. Our goal is to strengthen existing SGNN allowing them to withstand edge noises by extracting robust representations for signed graphs. First, we analyze the expressiveness of SGNN using an extended Weisfeiler-Lehman (WL) graph isomorphism test and identify the limitations to SGNN over triangles that are unbalanced. Then, we design some structure-based regularizers to be used in conjunction with an SGNN that highlight intrinsic properties of a signed graph. The tools and insights above allow us to propose a novel framework, Robust Signed Graph Neural Network (RSGNN), which adopts a dual architecture that simultaneously denoises the graph while learning node representations. We validate the performance of our model empirically on four real-world signed graph datasets, i.e., Bitcoin_OTC, Bitcoin_Alpha, Epinion and Slashdot, RSGNN can clearly improve the robustness of popular SGNN models. When the signed graphs are affected by random noise, our method outperforms baselines by up to 9.35% Binary-F1 for link sign prediction. Our implementation is available in PyTorch1.