Zhen Yang, Wei Du, Jie Wang, Wenze Zhou, Xiangfeng Meng, Zhengyang Wang, Suping Sun, Ziwei Du, Haodong Zou, Jie Chen, Yongbin Liu, Shicheng Tan, Jiahao Ying, Shu Zhao. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Knowledge Base Question Answering (KBQA) leverages structured knowledge bases to offer superior interpretability and hallucination resistance, making it a critical technology for precise knowledge reasoning.However, the prevailing LLM-based generate-then-execute formulation of semantic parsing is limited by strict syntactic constraints, making it primarily prone to structural deviations that render queries unexecutable, while suffering from semantic deviations that yield incorrect execution results.To address these challenges, we propose the Execution as Verification (EVER) framework, reframing semantic parsing as an iterative, self-correcting reasoning process driven by execution feedback.First, motivated by the insight that query executability serves as a strong proxy for answer correctness, we introduce Fine-Grained Execution-Aware Planning.This mechanism decomposes complex semantic parsing into a sequence of stepwise reasoning processes oriented by executability verification, ensuring high query executability.We further design a Self-Guided Semantic Correction mechanism based on execution result verification, utilizing execution feedback to verify and calibrate semantic deviations, thereby ensuring the semantic correctness of executable queries.Experimental results on the WebQSP and CWQ datasets demonstrate that our method achieves significant improvements in both query executability and answer accuracy, achieving stateof-the-art performance, particularly in complex multi-hop scenarios.Our code is available at https://github.com/ahu-zmh/EVER.

With the proliferation of social media, assessing depression risk through user-generated posts has emerged as a critical and prominent research direction. However, existing methods fail to capture clinically relevant linguistic markers due to ungrounded semantic representations in non-expert models. Therefore, this paper proposes a model, ProDoKE, which leverages prompt chaining integrated with domain knowledge to facilitate in-depth extraction and interpretation of clinically significant linguistic and semantic cues from social media posts. ProDoKE comprises two key modules, consisting of a prompt chaining enhancing module and a capsule fusion module. Following the collection of risky posts, the prompt chaining module systematically integrates domain-specific knowledge with Large Language Models (LLMs). This structured reasoning process guides the LLM to identify relevant clinical symptoms and then infer their underlying emotional states, resulting in a richer and more accurate interpretation. Furthermore, the capsule fusion module incorporates contrastive learning to optimize the alignment of emotional and symptom semantics, while using capsule networks to fortify the model's robustness. Evaluations on the eRisk2017 and eRisk2018 datasets show that ProDoKE achieves state-of-the-art performance, significantly outperforming previous baseline models in F1 scores.

The use of graph-based Retrieval-Augmented Generation (RAG) to retrieve relevant information from an external Knowledge Graph (KG) enables Large Language Model (LLMs) to answer specialty medical questions over private medical documents. However, medical Graph RAG only focuses on improving the response quality of LLMs via enhancing queries indiscriminately with local retrieved information, ignoring the long-tail medical knowledge that LLMs really need for the query, failing to answer global questions on a coarse-grained corpus. In this paper, we design a multi-granularity graph-based RAG framework for specialty medicine called SpecMedRAG, composed of MultiGranularity Knowledge and Multi-Route Retrieval. Specifically, Multi-Granularity Knowledge extracts specialty medical KG from private medical documents and links it with existing general medical KG to build fine-grained KG. Then it generates coarsegrained summaries from fine-grained KG. Multi-Route Retrieval comprises two retrieval paths: Local Retrieval enhanced by Long-tail Weight, which improves the utilization rate of longtail medical knowledge, and Global Retrieval, to reduce the reliance on the local detail of the query results by coarse-grained summaries. SpecMedRAG achieves SOTA results on specialty medical benchmark and demonstrate its universality on general benchmarks, significantly improving the accuracy of LLMs in specialty medical applications.

As the mainstream approach, LLMs have been widely applied and researched in TableQA tasks. Currently, the core of LLM-based TableQA methods typically include three phases: question decomposition, sub-question TableQA reasoning, and answer verification. However, several challenges remain in this process: i) Sub-questions generated by these methods often exhibit significant gaps with the original question due to critical information overlooked during the LLM's direct decomposition; ii) Verification of answers is typically challenging because LLMs tend to generate optimal responses during self-correct. To address these challenges, we propose a Triple-Inspired Decomposition and vErification (TIDE) strategy, which leverages the structural properties of triples to assist in decomposition and verification in TableQA. The inherent structure of triples (head entity, relation, tail entity) requires the LLM to extract as many entities and relations from the question as possible. Unlike direct decomposition methods that may overlook key information, our transformed sub-questions using triples encompass more critical details. Additionally, this explicit structure facilitates verification. By comparing the triples derived from the answers with those from the question decomposition, we can achieve easier and more straightforward validation than when relying on the LLM's self-correct tendencies. By employing triples alongside established LLM modes, Direct Prompting and Agent modes, TIDE achieves state-of-the-art performance across multiple TableQA datasets, demonstrating the effectiveness of our method.

Table Question Answering (TableQA) combines natural language understanding and structured data reasoning, posing challenges in semantic interpretation and logical inference. Recent advances in Large Language Models (LLMs) have improved TableQA performance through Direct Prompting and Agent paradigms. However, these models often rely on spurious correlations, as they tend to overfit to token co-occurrence patterns in pretraining corpora, rather than perform genuine reasoning. To address this issue, we propose Causal Intervention TableQA (CIT), which is based on a structural causal graph and applies front-door adjustment to eliminate bias caused by token co-occurrence. CIT formalizes TableQA as a causal graph and identifies token co-occurrence patterns as confounders. By applying front-door adjustment, CIT guides question variant generation and reasoning to reduce confounding effects. Experiments on multiple benchmarks show that CIT achieves state-of-the-art performance, demonstrating its effectiveness in mitigating bias. Consistent gains across various LLMs further confirm its generalizability.

Depression detection on social media aims to identify depressive tendencies within textual posts, providing timely intervention by the early detection of mental health issues . In predominant approaches, the Pre-trained Language Models(PLMs) are trained solely on public datasets, falling short of vertical scenarios due to insufficient domain-specific and commonsense knowledge . In addition, ambiguous commonsense knowledge could be misleading to PLMs and results in false judgments . Therefore, it poses significant challenges to select commonsense knowledge that is trusted. To address this, we propose CoKE, a model that incorporates trusted commonsense knowledge based on three-way decision theory to enhance depression detection. CoKE comprises three key modules: trusted screening, knowledge generation, and knowledge fusion. First, we utilize psychiatric clinical scales and three-way decision theory to screen out the uncertain domain from the massive user posts. Then, an adaptive framework is applied to generate and refine trusted commonsense knowledge that can explain the true semantics of posts in the uncertain domain. Finally, a dynamic integration of posts with highly trusted knowledge is achieved through a gating mechanism, resulting in embeddings enhanced by trusted commonsense knowledge that are more effective in determining depressive tendencies. We evaluate our model on two prominent datasets, eRisk2017 and eRisk2018, demonstrating its superiority over previous state-of-the-art baseline models .

Synthesis of myocardial infarction enhancement (MIE) images without contrast agents (CAs) has shown great potential to advance myocardial infarction (MI) diagnosis and treatment. It provides results comparable to late gadolinium enhancement (LGE) images, thereby reducing the risks associated with CAs and streamlining clinical workflows. The existing knowledge-and-data-driven approach has made progress in addressing the complex challenges of synthesizing MIE images (i.e., invisible myocardial scars and high inter-individual variability) but still has limitations in the interpretability of kinematic inference, morphological knowledge integration, and kinematic-morphological fusion, thereby reducing the transparency and reliability of the model and causing information loss during synthesis. In this paper, we proposed a knowledge-driven interpretative conditional diffusion model (K-ICDM), which learns kinematic and morphological information from non-enhanced cardiac MR images (CINE sequence and T1 sequence) guided by cardiac knowledge, enabling the synthesis of MIE images. Importantly, our K-ICDM introduces three key innovations that address these limitations, thereby providing interpretability and improving synthesis quality. (1) A novel cardiac causal intervention that generates counterfactual strain to intervene in the inference process from motion maps to abnormal myocardial information, thereby establishing an explicit relationship and providing the clear causal interpretability. (2) A knowledge-driven cognitive combination strategy that utilizes cardiac signal topology knowledge to analyze T1 signal variations, enabling the model to understand how to learn morphological features, thus providing interpretability for morphology capture. (3) An information-specific adaptive fusion strategy that integrates kinematic and morphological information into the conditioning input of the diffusion model based on their specific contributions and adaptively learns their interactions, thereby preserving more detailed information. Experiments on a broad MI dataset with 315 patients show that our K-ICDM achieves state-of-the-art performance in contrast-free MIE image synthesis, improving structural similarity index measure (SSIM) by at least 2.1% over recent methods. These results demonstrate that our method effectively overcomes the limitations of existing methods in capturing the complex relationship between myocardial motion and scar distribution and integrating of static and dynamic sequences, thus enabling the accurate synthesis of subtle scar boundaries.

Abstract Prompt transfer is a transfer learning method based on prompt tuning, which enhances the parameter performance of prompts in target tasks by transferring source prompt embeddings. Among existing methods, weighted aggregation is effective and possesses the advantages of being lightweight and modular. However, these methods may transfer redundant or irrelevant information from the source prompts to the target prompt, leading to negative impacts. To alleviate this problem, we propose Prompt Contrastive Transformation (PCT), which achieves efficient prompt transfer through prompt contrastive transformation and attentional fusion. PCT transforms the source prompt into task-agnostic embedding and task-specific embeddings through singular value decomposition and contrastive learning, reducing information redundancy among source prompts. The attention module in PCT selects more effective task-specific embeddings and fuses them with task-agnostic embedding into the target prompt. Experimental results show that, despite tuning only 0.035% of task-specific parameters, PCT achieves improvements in prompt transfer for single target task adaptation across various NLP tasks.

To meet the challenge of incompleteness within Knowledge Graphs, Knowledge Graph Embedding (KGE) has emerged as the fundamental methodology for predicting the missing link (Link Prediction), by mapping entities and relations as low-dimensional vectors in continuous space. However, current KGE models often struggle with the polysemy issue, where entities exhibit different semantic characteristics depending on the relations in which they participate. Such limitation stems from weak interactions between entities and their relation contexts, leading to low expressiveness in modeling complex structures and resulting in inaccurate predictions. To address this, we propose Contextualized Quaternion Embedding (ConQuatE), a model that enhances the representation learning of entities across multiple semantic dimensions by leveraging quaternion rotation to capture diverse relational contexts. In specific, ConQuatE incorporates contextual cues from various connected relations to enrich the original entity representations. Notably, this is achieved through efficient vector transformations in quaternion space, without any extra information required other than original triples. Experimental results demonstrate that our model outperforms state-of-the-art models for Link Prediction on four widely recognized datasets: FB15k-237, WN18RR, FB15k, and WN18.

Abstractive summarization has made significant progress in recent years, which aims to generate a concise and coherent summary that contains the most important facts from the source document. Current fine-tuning approaches based on pre-training models typically rely on autoregressive and maximum likelihood estimation, which may result in inconsistent historical distributions generated during the training and inference stages, i.e., exposure bias problem. To alleviate this problem, we propose a hybrid fine-tuning model(HyFit), which combines contrastive learning and reinforcement learning in a diverse sampling space. Firstly, we introduce reparameterization and probability-based sampling methods to generate a set of summary candidates called candidates bank, which improves the diversity and quality of the decoding sampling space and incorporates the potential for uncertainty. Secondly, hybrid fine-tuning with sampled candidates bank, upweighting confident summaries and downweighting unconfident ones. Experiments demonstrate that HyFit significantly outperforms the state-of-the-art models on SAMSum and DialogSum. HyFit also shows good performance on low-resource summarization, on DialogSum dataset, using only approximate 8% of the examples exceed the performance of the base model trained on all examples.

The primary objective of knowledge tracing (KT) is to evaluate students’ understanding and mastery of knowledge through their responses to exercises, which aids in predicting their future performance. Deep neural networks have been widely applied in the area of knowledge tracing and have demonstrated encouraging results. Nevertheless, in real-world scenarios, there is a substantial amount of noise in students’ response records. These noises may amplify the inherent risk of overfitting in deep neural networks, leading to a decrease in model performance. To address these issues, we introduce a new model called filter knowledge tracing (FKT). This innovative model incorporates a learnable filter into KT to filter out noise information from students’ exercise sequences. We redefine the input paradigm of the data, using learnable filters to perform filtering operations in its frequency domain representation space, effectively removing noise. Additionally, an attention module has been introduced in the FKT model to evaluate the impact of students’ historical interactions on their current knowledge state. To validate our model, we conduct extensive experiments utilizing four publicly available datasets. The results demonstrate that FKT outperforms existing benchmarks, particularly on larger datasets, signifying an improvement in KT performance while effectively reducing the risk of overfitting.

Emoji are often used in social media to enrich users’ emotions, and they play an important role in the task of social media sentiment analysis. In practice, researchers are more likely to consider emoji as special symbols and treat them separately from the text. Some existing methods use emoji as a dictionary for matching or converting emoji into text. However, these methods disregard the relationship between emoji and context, blue and they do not reflect the emotions that users are expected to express. It is challenging to incorporate the original emotions of emoji in social media sentiment analysis. In this article, we propose the EPE model: Emoji Pre-trained feature Enhanced sentiment analysis. Specifically, we collected 8 million tweets and selected 5 million tweets with pre-trained emoji with context using the BERT model. We labeled 20,000 tweets as a three-category dataset and used Bi-LSTM with an attention layer to extract text features. Emoji were retained as key emotion information and combined with text features in the final layer as a connected vector for final prediction. Experimental results with our dataset showed that the proposed EPE model achieved better performance than other baseline models.

Emotion recognition in conversation (ERC) is challenging because the conversation takes place in real time and the speakers interact with each other. However, existing methods ignore the dynamic characteristics of interaction between speakers, and the problem of long-range context propagation still exists. In this article, we propose a dynamic interaction emotion unit to solve the preceding problems on the transcription of the conversation. First, we propose a main influence interval search algorithm to provide a dynamic interaction interval for each utterance. Then, we utilize the speaker-aware influence module and the two-stream context module to capture the dynamic interaction and the contextual information from this interval. Furthermore, to obtain the speaker state representation rich in emotional information, we propose a novel dynamic routing algorithm to fuse the preceding information. These well-integrated state representations also enable our model to capture contextual information at a longer distance. Experiments on multiple datasets demonstrate the effectiveness of the proposed method.

The attacker's malicious behavior of injecting well-designed adversarial examples (i.e., fake users) into recommender systems will severely affect the security of systems. It's difficult to fully obtain details of victim recommendation models (i.e., black-box model) in practical recommendation scenarios, using the transferability of adversarial examples to achieve black-box attacks is still an effective way. At present, adversarial examples generated by existing gradient-based methods are prone to drop into local minima, making it impossible to achieve the expected attack effect and reducing the transferability. In this article, we propose an attack algorithm that enhances the transferability of adversarial examples based on the Nesterov Momentum for Recommendation Systems (ETANRS). With white-box recommendation surrogate models, we utilize Nesterov momentum to generate better adversarial examples, then inject them into black-box victim models to attack. We utilize the accumulated gradients and pre-determine the update direction of the gradients to keep the optimal value from being lost, thus enhancing the transferability of the adversarial examples. Experimental results demonstrate that our method is better than state-of-the-art gradient-based attack algorithms, which affect recommendation performance.

Recent studies have shown that Graph Neural Networks (GNNs) are vulnerable to well-designed and imperceptible adversarial attack. Attacks utilizing gradient information are widely used in the field of attack due to their simplicity and efficiency. However, several challenges are faced by gradient-based attacks: 1) Generate perturbations use white-box attacks (i.e., requiring access to the full knowledge of the model), which is not practical in the real world; 2) It is easy to drop into local optima; and 3) The perturbation budget is not limited and might be detected even if the number of modified edges is small. Faced with the above challenges, this article proposes a black-box adversarial attack method, named NAG-R, which consists of two modules known asNesterovAcceleratedGradient attack module andRewiring optimization module. Specifically, inspired by adversarial attacks on images, the first module generates perturbations by introducing Nesterov Accelerated Gradient (NAG) to avoid falling into local optima. The second module keeps the fundamental properties of the graph (e.g., the total degree of the graph) unchanged through a rewiring operation, thus ensuring that perturbations are imperceptible. Intensive experiments show that our method has significant attack success and transferability over existing state-of-the-art gradient-based attack methods.

Automatic segmentation of myocardial infarction (MI) regions in late gadolinium-enhanced cardiac magnetic resonance images is an essential step in the computed diagnosis of myocardial infarction. Most of the current myocardial infarction region segmentation methods are based on fully supervised deep learning. However, cardiologists' annotation of myocardial infarction regions in cardiac magnetic resonance images during the diagnosis process is time-consuming and expensive. This paper proposes a semi-supervised myocardial infarction segmentation. It consists of two models: 1) a boundary mining model and 2) an adversarial learning model. The boundary mining model can solve the boundary ambiguity problem by enlarging the gap between the foreground and background features, thus segmenting the myocardial infarction region accurately. The adversarial learning model can make the boundary mining model learn from additional unlabeled data by evaluating the segmentation performance and providing pseudo supervision, which significantly increases the robustness of the boundary mining model. We conduct extensive experiments on an in-house myocardial magnetic resonance dataset. The experimental results on six evaluation metrics demonstrate that our method achieves excellent results in myocardial infarction segmentation and outperforms the state-of-the-art semi-supervised methods.

Network representation learning, also called network embedding, aiming to learn low dimensional vectors for nodes while preserving essential properties of the network, benefits plenty of practical applications. However, how to do representation learning on the network quickly and effectively is a meaningful and challenging task, especially for the attributed networks. In this paper, we propose HANE, a Hierarchical Attributed Network Embedding framework, which is a fast and effective method by quickly constructing a hierarchical attributed network of different granularities to learn nodes representations. Specifically, for an attributed network, HANE first builds a hierarchy of successively smaller attributed network from fine to coarse by the fast granulation strategy fusing topological structure and node attributes. After using any unsupervised network embedding method to learn nodes representations of the coarsest network, HANE refines the nodes representations of the hierarchical attributed network from coarse to fine. HANE improves the speed of network representation learning while maintaining its performance and the representation learning method of the coarsest network is flexible. We conduct extensive evaluations for the proposed framework HANE on six datasets and two benchmark applications. Experimental results demonstrate that HANE achieves significant improvements over previous state-of-the-art network embedding methods in efficiency and effectiveness.

Network representation learning, also called network embedding, aiming to learn low dimensional vectors for nodes while preserving essential properties of the network, such as structural similarity, attribute similarity, etc. The low-dimensional vector of the node can be used as the input of the machine learning algorithm and applied to a lot of downstream tasks, such as node classification and link prediction, benefits plenty of practical applications.

Overlapping communities exist in real networks, where the communities represent hierarchical community structures, such as schools and government departments. A non-binary tree allows a vertex to belong to multiple communities to obtain a more realistic overlapping community structure. It is challenging to select appropriate leaf vertices and construct a hierarchical tree that considers a large amount of structural information. In this paper, we propose a non-binary hierarchical tree overlapping community detection based on multi-dimensional similarity. The multi-dimensional similarity fully considers the local structure characteristics between vertices to calculate the similarity between vertices. First, we construct a similarity matrix based on the first and second-order neighbor vertices and select a leaf vertex. Second, we expand the leaf vertex based on the principle of maximum community density and construct a non-binary tree. Finally, we choose the layer with the largest overlapping modularity as the result of community division. Experiments on real-world networks demonstrate that our proposed algorithm is superior to other representative algorithms in terms of the quality of overlapping community detection.

The paper-reviewer recommendation task is of significant academic importance for conference chairs and journal editors. It aims to recommend appropriate experts in a discipline to comment on the quality of papers of others in that discipline. How to effectively and accurately recommend reviewers for the submitted papers is a meaningful and still tough task. Generally, the relationship between a paper and a reviewer often depends on the semantic expressions of them. Creating a more expressive representation can make the peer-review process more robust and less arbitrary. So the representations of a paper and a reviewer are very important for the paper-reviewer recommendation. Actually, a reviewer or a paper often belongs to multiple research fields, which increases difficulty in paper-reviewer recommendation. In this article, we propose a Multi-Label Classification method using a HIErarchical and transPArent Representation named Hiepar-MLC . First, we introduce HIErarchical and transPArent Representation (Hiepar) to express the semantic information of the reviewer and the paper. Hiepar is learned from a two-level bidirectional gated recurrent unit based network applying the attention mechanism. It is capable of capturing the two-level hierarchical information (word-sentence-document) and highlighting the elements in reviewers or papers to support the labels. This word-sentence-document information mirrors the hierarchical structure of a reviewer or a paper and captures the exact semantics of them. Then we transform the paper-reviewer recommendation problem into a multi-level classification issue, whose multiple research labels exactly guide the learning process. It is flexible in that we can select any multi-label classification method to solve the paper-reviewer recommendation problem. Further, we propose a simple multi-label-based reviewer assignment (MLBRA) strategy to select the appropriate reviewers. It is interesting in that we also explore the paper-reviewer recommendation in the coarse-grain granularity. Extensive experiments on the real-world dataset consisting of the papers in the ACM Digital Library show that Hiepar-MLC achieves better label prediction performance than the existing representation alternatives. In addition, with the MLBRA strategy, we show the effectiveness and the feasibility of our transformation from paper-reviewer recommendation to multi-label classification.

Citation recommendation is of great value in scientific research during the era of big scholarly data. The task deals with different object types (e.g., paper, author, etc.) and relation types, which naturally constitute a heterogeneous information network (HIN). In order to capture semantic relations and attribute values on relations, we propose a Weighted Heterogeneous Information Network Containing Semantic Linking algorithm (WHIN-CSL) to recommend references. Firstly, we construct a weighted HIN consisting of two kinds of vertexes (papers and authors) and four kinds of relations (semantic linking, citing, writing and co-author). Secondly, we use the network representation learning method to obtain feature representation of each vertex, then we can compute the similarity between vertexes. Finally, we recommend references through linear combination among multimodal similarities. Experimental results on two real-world datasets show that WHIN-CSL achieves better performance because of the flexibly integrating information with the help of weighted HIN containing semantic linking.