Knowledge-based conversational question answering (KBCQA) confronts persistent challenges in resolving coreference, modeling contextual dependencies, and executing complex logical reasoning. Existing approaches, whether end-to-end semantic parsing or stepwise agent-based reasoning—often suffer from structural inaccuracies and prohibitive computational costs, particularly when processing intricate queries over large knowledge graphs. To address these limitations, we introduce SEAL, a novel two-stage semantic parsing framework grounded in self-evolving agentic learning.In the first stage, a large language model (LLM) extracts a minimal S-expression core that captures the essential semantics of the input query. This core is then refined by an agentic calibration module, which corrects syntactic inconsistencies and aligns entities and relations precisely with the underlying knowledge graph. The second stage employs template-based completion, guided by question-type prediction and placeholder instantiation, to construct a fully executable S-expression. This decomposition not only simplifies logical form generation but also significantly enhances structural fidelity and linking efficiency.Crucially, SEAL incorporates a self-evolving mechanism that integrates local and global memory with a reflection module, enabling continuous adaptation from dialog history and execution feedback without explicit retraining. Extensive experiments on the SPICE benchmark demonstrate that SEAL achieves state-of-the-art performance, especially in multi-hop reasoning, comparison, and aggregation tasks. The results validate notable gains in both structural accuracy and computational efficiency, underscoring the framework's capacity for robust and scalable conversational reasoning.

Medical education relies heavily on Simulated Patients (SPs) to provide a safe environment for students to practice clinical skills, including medical image analysis. However, the high cost of recruiting qualified SPs and the lack of diverse medical imaging datasets have presented significant challenges. To address these issues, this paper introduces MedDiT, a novel knowledge-controlled conversational framework that can dynamically generate plausible medical images aligned with simulated patient symptoms, enabling diverse diagnostic skill training. Specifically, MedDiT integrates various patient Knowledge Graphs (KGs), which describe the attributes and symptoms of patients, to dynamically prompt Large Language Models' (LLMs) behavior and control the patient characteristics, mitigating hallucination during medical conversation. Additionally, a well-tuned Diffusion Transformer (DiT) model is incorporated to generate medical images according to the specified patient attributes in the KG. In this paper, we present the capabilities of MedDiT through a practical demonstration, showcasing its ability to act in diverse simulated patient cases and generate the corresponding medical images. This can provide an abundant and interactive learning experience for students, advancing medical education by offering an immersive simulation platform for future healthcare professionals. The work sheds light on the feasibility of incorporating advanced technologies like LLM, KG, and DiT in education applications, highlighting their potential to address the challenges faced in simulated patient-based medical education.

Attribute extraction aims to identify attribute names and the corresponding values from descriptive texts, which is the foundation for extensive downstream applications such as knowledge graph construction, search engines, and e-Commerce.In previous studies, attribute extraction is generally treated as a classification problem for predicting attribute types or a sequence tagging problem for labeling attribute values, where two paradigms, i.e., closed-world and open-world assumption, are involved.However, both of these paradigms have limitations in terms of real-world applications.And prior studies attempting to integrate these paradigms through ensemble, pipeline, and co-training models, still face challenges like cascading errors, high computational overhead, and difficulty in training.To address these existing problems, this paper presents Attribute Tree, a unified formulation for realworld attribute extraction application, where closed-world, open-world, and semi-open attribute extraction tasks are modeled uniformly.Then a text-to-tree generation model, AtTGen, is proposed to learn annotations from different scenarios efficiently and consistently.Experiments demonstrate that our proposed paradigm well covers various scenarios for real-world applications, and the model achieves state-ofthe-art, outperforming existing methods by a large margin on three datasets.Our code, pretrained model, and datasets are available at https://github.com/lsvih/AtTGen.

Document-level relation extraction (DocRE)aims to extract relations among entities within a document, which is crucial for applications like knowledge graph construction.Existing methods usually assume that entities and their mentions are identified beforehand, which falls short of real-world applications.To overcome this limitation, we propose TAG, a novel tableto-graph generation model for joint extraction of entities and relations at document-level.To enhance the learning of task dependencies, TAG induces a latent graph among mentions, with different types of edges indicating different task information, which is further broadcast with a relational graph convolutional network.To alleviate the error propagation problem, we adapt the hierarchical agglomerative clustering algorithm to back-propagate task information at decoding stage.Experiments on the benchmark dataset, DocRED, demonstrate that TAG surpasses previous methods by a large margin and achieves state-of-the-art results 1 .

As a fundamental subtask of Knowledge Base Question Answering (KBQA), Relation Detection (KBQA-RD) plays a crucial role to detect the KB relations between entities or variables in natural language questions. It remains, however, a challenging task, particularly for significant large-scale relations and in the presence of easily confused relations. Recent state-of-the-art methods not only struggle with such scenarios, but often take into account only one facet and fail to incorporate the subtle discrepancy among the relations. In this paper, we propose a simple and efficient three-stage framework to exploit the coarse-to-fine paradigm. Specifically, we employ a natural clustering over all KB relations and perform a coarse-to-fine relation recognition process based on the relation clustering. In this way, our framework (i.e., CORD) refines the detection of relations, so as to scale well with large-scale relations. Experiments on both single-relation (i.e., SimpleQuestions (SQ)) and multi-relation (i.e., WebQSP (WQ)) benchmarks show that CORD not only achieves the outstanding relation detection performance in KBQA-RD subtask; but more importantly, further improves the accuracy of KBQA systems.

The long-tail effect is a common issue that limits the performance of deep learning models on real-world datasets. Character image datasets are also affected by such unbalanced data distribution due to differences in character usage frequency. Thus, current character recognition methods are limited when applied in the real world, especially for the categories in the tail that lack training samples, e.g., uncommon characters. In this paper, we propose a zero-shot character recognition framework via radical-based reasoning, called RZCR, to improve the recognition performance of few-sample character categories in the tail. Specifically, we exploit radicals, the graphical units of characters, by decomposing and reconstructing characters according to orthography. RZCR consists of a visual semantic fusion-based radical information extractor (RIE) and a knowledge graph character reasoner (KGR). RIE aims to recognize candidate radicals and their possible structural relations from character images in parallel. The results are then fed into KGR to recognize the target character by reasoning with a knowledge graph. We validate our method on multiple datasets, and RZCR shows promising experimental results, especially on few-sample character datasets.

Recent years have witnessed the transition from sentence-level to document-level in relation extraction (RE), with new formulation, new methods and new insights. Yet, the fundamental concept, mention, is not well-considered and well-defined. Current datasets usually use automatically-detected named entities as mentions, which leads to the missing reference problem. We show that such phenomenon hinders models' reasoning abilities. To address it, we propose to incorporate coreferences (e.g. pronouns and common nouns) into mentions, based on which we refine and re-annotate the widely-used DocRED benchmark as R-DocRED. We evaluate various methods and conduct thorough experiments to demonstrate the efficacy of our formula. Specifically, the results indicate that incorporating coreferences helps reduce the long-term dependencies, further improving models' robustness and generalization under adversarial and low-resource settings. The new dataset is made publicly available for future research.

Yanzeng Li, Jiangxia Cao, Xin Cong, Zhenyu Zhang, Bowen Yu, Hongsong Zhu, Tingwen Liu. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Keyphrase extraction (KPE) aims to obtain a set of phrases from a document that can summarize the main content of the document.Recently, pre-trained language models (LMs), especially BERT and ELMo, have achieved remarkable success, presenting new state-of-the-art results in unsupervised KPE.However, current pre-trained LMs focus on building language modeling objectives to learn a general representation, ignoring the keyphrase-related knowledge.Intuitively, the joint embedding of the keyphrase set should tend to be close to that of the extracted document, and far from those of other documents.In this work, we propose a contrastive learning-based semantic representation task to further improve BERT for unsupervised KPE.Particularly, we design a doc-phrase attention module to generate joint semantic embedding of the keyphrase set as a positive sample and select other semantically similar documents as hard negative samples.In the prediction layer, we further add an accumulated self-attention module to calculate the final scores of candidate phrases.We compare with eight strong baselines, and evaluate our model on three publicly available datasets.Experimental results show that our model is effective and robust on both long and short documents.

Attribute extraction is a task to identify the attribute and the corresponding attribute value from unstructured text, which is important for extensive applications like web information retrieval and the recommended system. The traditional relation extraction-based methods or joint extraction-based systems are often perform attribute classify based on subject and attribute-value pairs, and extract the attribute triples in the scope of ontology schema categories, which is in the assumption of the close-world and cannot satisfy the diversity of attributes. In this work, we propose a semi-open information extraction system for attribute extraction in a multi-component framework. With the proposed semi-open attribute extraction system (SOAE), more attribute-value pairs can be discovered by extracting literal triples without the limitation of pre-defined ontology. An additional co-trained ontology-based attribute extraction model is appended as a component following the assumption of the partial-closed world (PCWA), remission the performance degradation of SOAE caused by missing of the literal predicate in raw text and contribute to extract richer attribute triples and construct more dense knowledge graph. For evaluating the performance of the attribute extraction system, we construct a Chinese functional description text dataset CNShipNet and conduct experiments on it. The experimental results demonstrate that our proposed approach outperforms several state-of-the-art baselines with a large margin.

Most Chinese pre-trained models take character as the basic unit and learn representation according to character's external contexts, ignoring the semantics expressed in the word, which is the smallest meaningful utterance in Chinese.Hence, we propose a novel wordaligned attention to exploit explicit word information, which is complementary to various character-based Chinese pre-trained language models.Specifically, we devise a pooling mechanism to align the character-level attention to the word level and propose to alleviate the potential issue of segmentation error propagation by multi-source information fusion.As a result, word and character information are explicitly integrated at the fine-tuning procedure.Experimental results on five Chinese NLP benchmark tasks demonstrate that our method achieves significant improvements against BERT, ERNIE and BERT-wwm.

Self-attention based models like Transformer have achieved great success on kinds of Natural Language Processing tasks. However, the traditional fixed fully-connected structure faces many challenges in practice, such as computing redundancy, fixed granularity, and inexplicable. In this paper, we present BiG-Transformer, which employs attention with bipartite-graph structure to replace the fully-connected self-attention mechanism in Transformer. Specifically, two parts of the graph are designed for integrating hierarchical semantic information, and two types of connection are proposed to fuse information from different positions. Experiments on four tasks show the BiG-Transformer achieves better performance compared to Transformer liked models and Recurrent Neural Networks.

In a website fingerprinting attack, an eavesdropper analyses the traffic between the Tor user and entry node of the Tor network to infer which websites the user has visited. Some recent work apply deep learning algorithms, however, most of them do not fully exploit the packet timing information. In this work, we propose a novel website fingerprinting attack based on a two-channel Temporal Convolutional Networks model that extracts features from both the packet sequences and packet timing information. Our attack is proved to perform better compared to the state-of-the-art attacks. Experiment results also show that the timing information is very useful for classification. Furthermore, we collect our own traffic traces between client and entry node, and transform them into three extraction layers: TCP, TLS and Tor cell layer, and meanwhile record Tor’s cell log at the entry node. The experimental results show that the data of the cell layer is the most divisible among the three layers. Based on the experimental results, we conclude that the adversary at the entry node has an advantage over the one who just listens to traffic between client and entry node.

Functional description information refers to the texts that describe the functionality or performance characteristics of a certain object. This type of information is of great potential value for the field of intelligence discovery. Thus automatically and accurately identifying this information from large amounts of texts on the web is very important. In this paper we reduce the functional description problem to a binary classification task deciding whether the input sentence is a functional description sentence or not. However, there exist lots of comment texts in the web data, which are semantically very similar to description texts, making our task quite difficult. Also, existing methods only provide general sentence representation models, which can't lead to targeted ways to solve our problem. Therefore, to address the problem, we not only exploit contexts, like many other previous work did, but also introduce indicator word information to learn rich representations. And in order to incorporate them both, we propose two models, namely ICNet(multi-tasks) and ICNet(ensemble). ICNet(multitasks) exploits them jointly in a integrated process of learning representations, while ICNet(ensemble) exploits them by two respective but concatenated sub-models. Experimental results on the collected real-world dataset indicate that both ICNet(multitasks) and ICNet(ensemble) achieve higher F1 scores compared with FaxtText, CNN, RNN, LSTM and Bi-LSTM, QuickThought models on this task.

Opinion target extraction (OTE) is a fundamental step for sentiment analysis and opinion summarization. We analyze the difference between Chinese and the Indo-European languages family, and reduce Chinese OTE to a character-based sequence tagging task. Then we introduce two novel features for each character by distributing POS differentially and using predefined templates over contexts and dictionaries. We further propose a character-based BiLSTM-CRF model incorporating the two feature sequences aligned with the character sequence. Experimental results on real-world consumer review datasets show that our work significantly outperforms the baseline methods for Chinese OTE.