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- Detect Rumors Using Time Series of Social Context Information on Microblogging Websites680
A novel approach to capture the temporal characteristics of features related to microblog contents, users and propagation patterns based on the time series of rumor's lifecycle, for which time series modeling technique is applied to incorporate various social context information.
- K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters648
K-Adapter is proposed, which remains the original parameters of the pre-trained model fixed and supports continual knowledge infusion and captures richer factual and commonsense knowledge than RoBERTa.
- Simplify the Usage of Lexicon in Chinese NER311
This work proposes a simple but effective method for incorporating the word lexicon into the character representations of Chinese named entity recognition using word lexicons, and shows that the proposed method can be easily incorporated with pre-trained models like BERT.
- Incorporating Corporation Relationship via Graph Convolutional Neural Networks for Stock Price Prediction265
Experiments on the data collected from stock market in Mainland China show that the representation learned from the model is able to capture relationships between corporations, and prediction models incorporating related corporations' information are able to make more accurate predictions on stock market.
- Task-oriented Dialogue System for Automatic Diagnosis255
Experimental results on this dialogue system show that additional symptoms extracted from conversation can greatly improve the accuracy for disease identification and the dialogue system is able to collect these symptoms automatically and make a better diagnosis.
- A Lexicon-Based Graph Neural Network for Chinese NER249
A lexicon-based graph neural network with global semantics is introduced, in which lexicon knowledge is used to connect characters to capture the local composition, while a global relay node can capture global sentence semantics and long-range dependency.
- DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services183
Quantitative and qualitative results on DISC-Law-Eval demonstrate the effectiveness of the system in serving various users across diverse legal scenarios, and enhances models' ability to access and utilize external legal knowledge.
- An end-to-end heterogeneous graph representation learning-based framework for drug–target interaction prediction179
A novel 'end-to-end' learning-based framework based on heterogeneous 'graph' convolutional networks for 'DTI' prediction called end- to-end graph (EEG)-DTI, which performs better than existing state-of-art methods for identifying DTIs.
- A learning-based framework for miRNA-disease association identification using neural networks178
A novel learning-based framework, MDA-CNN, is proposed, which outperforms some state-of-the-art approaches in a large margin on both tasks of miRNA-disease association prediction and mi RNA-phenotype association prediction.
- A Partition Filter Network for Joint Entity and Relation Extraction172
A partition filter network to model two-way interaction between tasks properly, where feature encoding is decomposed into two steps: partition and filter, and auxiliary experiments suggest that relation prediction is contributory to named entity prediction in a non-negligible way.
- Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training149
Supervised Contrastive Pre-training on large-scale sentiment-annotated corpora retrieved from in-domain language resources leads to better capture of both implicit and explicit sentiment orientation towards aspects in reviews, and comprehensive analysis validates its effectiveness on learning implicit sentiment.
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- From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents116
A comprehensive survey of simulation driven by large language models, illustrating the recent progress in simulation driven by LLM-empowered agents and categorizing the simulations into three types: Individual Simulation, which mimics specific individuals or demographic groups; Scenario Simulation, where multiple agents collaborate to achieve goals within specific contexts; and Society Simulation, which models interactions within agent societies.
- Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text94
The results show that the proposed logic-driven data augmentation algorithm achieves state-of-the-art performance on both datasets, and even surpasses human performance on the ReClor dataset.
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- A benchmark for automatic medical consultation system: frameworks, tasks and datasets93
This article creates a new large medical dialogue dataset with multi-level fine-grained annotations and establishes five independent tasks, including named entity recognition, dialogue act classification, symptom label inference, medical report generation and diagnosis-oriented dialogue policy.
- Unsupervised Discovery of Discourse Relations for Eliminating Intra-sentence Polarity Ambiguities92
Experimental results showed that the proposed methods not only effectively recognized the defined discourse relations but also achieved significant improvement by integrating discourse information in sentence-level polarity classification.
- SC2disease: a manually curated database of single-cell transcriptome for human diseases88
The literature prior to March 2020 for studies which used scRNA-seq to study diseases with human samples are reviewed, and the SC2disease database is developed to summarize all the data by different diseases, tissues and cell types.
- DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning85
This work proposes Multiple Experts Fine-tuning Framework, a methodology that improves general LLMs by endowing them with multi-turn question answering abilities, domain text processing capabilities, mathematical computation skills, and retrieval-enhanced generation capabilities.
- Mask Attention Networks: Rethinking and Strengthen Transformer83
A novel understanding of SAN and FFN are presented as Mask Attention Networks (MANs) and it is shown that they are two special cases of MANs with static mask matrices, which limit the capability for localness modeling in text representation learning.
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- Storytelling from an Image Stream Using Scene Graphs78
This work proposes a novel graph-based architecture for visual storytelling by modeling the two-level relationships on scene graphs and results indicate that this method achieves state-of-the-art results.
- Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning77
A reinforcement learning framework for essay scoring that incorporates quadratic weighted kappa as guidance to optimize the scoring system and experiment results show the effectiveness of this framework.
- Enhancing Dialogue Symptom Diagnosis with Global Attention and Symptom Graph75
A global attention mechanism to capture more symptom related information, and a symptom graph to model the associations between symptoms rather than treating each symptom independently are proposed and shown to be effective to boost dialogue symptom diagnosis.
- Math Word Problem Solving with Explicit Numerical Values63
A novel approach called NumS2T is proposed, which enhances math word problem solving performance by explicitly incorporating numerical values into a sequence-to-tree network.
- DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation59
A new dialog pre-training framework called DialogVED is proposed, which introduces continuous latent variables into the enhanced encoder-decoder pre- training framework to increase the relevance and diversity of responses.
- A Question Type Driven Framework to Diversify Visual Question Generation58
A question type driven framework to produce multiple questions for a given image with different focuses is proposed, which outperforms the state-of-the-art approaches in terms of both relevance and diversity.
- GPT-4V(ision) as A Social Media Analysis Engine54
GPT-4V shows effectiveness in these tasks, exhibiting capabilities like joint image–text understanding, contextual and cultural awareness, and commonsense knowledge application, however, challenges persist, including struggles with multilingual social multimedia comprehension and difficulty in adapting to the latest social media trends.
- Using External Knowledge for Financial Event Prediction Based on Graph Neural Networks51
A novel financial event prediction task that takes a historical event chain as input and predicts what event will happen next and a gated graph neural network based approach is utilized to capture complicated relationships between event graphs for better event prediction.
- A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators51
This work argues that natural questions need to have two specific attributes from the perspectives of content and linguistic respectively, namely, natural and human-written, and proposes two discriminators, one for each attribute, to enhance the training.
- An evaluation of learning analytics to identify exploratory dialogue in online discussions51
A self-training framework that employs discourse features and topical features for classification by integrating both cue-phrase matching and k-nearest neighbour classification is described, which outperforms other approaches.
- A Multi-Agent Communication Framework for Question-Worthy Phrase Extraction and Question Generation46
A multi-agent communication framework is introduced, taking phrase extraction and question generation as two agents, and learn these two tasks simultaneously via message passing mechanism and can extract question-worthy phrases, which are able to improve the performance of question generation.
- Utilizing Microblogs for Automatic News Highlights Extraction37
A novel method to improve news highlights extraction by using microblogs based on the hypothesis that microblog posts, although noisy, are not only indicative of important pieces of information in the news story, but also inherently “short and sweet” resulting from the artificial compression effect due to the length limit.
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- An Enhanced Knowledge Injection Model for Commonsense Generation32
This work integrates two additional modules into the pretrained encoder-decoder model for prototype modeling to enhance the knowledge injection procedure and shows results that significantly improves the performance on all the metrics.
- A Novel Approach to Component Assembly Inspection Based on Mask R-CNN and Support Vector Machines32
A method of assembly inspection based on machine vision and a deep neural network that effectively completes the identification of missing and misaligned parts in the assembly, and has good robustness.
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- TCIC: Theme Concepts Learning Cross Language and Vision for Image Captioning30
A Theme Concepts extended Image Captioning (TCIC) framework that incorporates theme concepts to represent high-level cross-modality semantics is proposed and experimental results on MS COCO show the effectiveness of the approach compared to some state-of-the-art models.
- Debatrix: Multi-dimensional Debate Judge with Iterative Chronological Analysis Based on LLM29
Debatrix is proposed, which makes the analysis and assessment of multi-turn debates more aligned with majority preferences by leveraging Large Language Models (LLMs), and features a vertical, iterative chronological analysis and a horizontal, multi-dimensional evaluation collaboration.
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- Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model28
A novel co-attention mechanism based neural network is proposed to capture the interactions between participants on argument level and significantly outperforms some state-of-the-art methods for persuasiveness evaluation.
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- Bridging by Word: Image Grounded Vocabulary Construction for Visual Captioning27
A novel hierarchical structure is proposed to construct an image-grounded vocabulary, based on which, captions are generated with limitation and guidance, and the effectiveness of this framework compared to some state-of-the-art models is shown.
- MVPTR26
This paper proposes to learn Multi-level semantic alignment for Vision-language Pre-TRaining (MVPTR), a nested structure of both modalities to introduce concepts as high-level semantics and achieves state-of-the-art results on several vision and language tasks.
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- Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation24
This paper presents the ArgTersely benchmark for sentence-level counter-argument generation, drawing from a manually annotated dataset from the ChangeMyView debate forum, and proposes Arg-LlaMA for generating high-quality counter-argument.
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- Unifying Cross-Lingual and Cross-Modal Modeling Towards Weakly Supervised Multilingual Vision-Language Pre-training22
It is argued that the universal multilingual representation learned from texts allows the cross-modal interaction learned in English to be transferable to other languages, and a framework to effectively unify cross-lingual and cross- modal pre-training is proposed.
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- PathQG: Neural Question Generation from Facts22
This paper presents a novel task of question generation given a query path in the knowledge graph constructed from the input text, and formulate query representation learning as a sequence labeling problem for identifying the involved facts to form a query and employ an RNN-based generator for question generation.
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- ReForm-Eval: Evaluating Large Vision Language Models via Unified Re-Formulation of Task-Oriented Benchmarks17
This work proposes to re-formulate existing benchmarks into unified LVLM-compatible formats and presents ReForm-Eval benchmark, offering substantial data for evaluating various capabilities of LVLMs, demonstrating the comprehensiveness and reliability of ReForm-Eval in assessing various LVLMs.
- Discrete Argument Representation Learning for Interactive Argument Pair Identification17
Experimental results on the large-scale dataset collected from CMV show that the proposed framework can significantly outperform the competitive baselines and reveal why the model yields superior performance and prove the usefulness of the learned representations.
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- SoMeLVLM: A Large Vision Language Model for Social Media Processing16
A Large Vision Language Model for Social Media Processing (SoMeLVLM) is introduced, which is a cognitive framework equipped with five key capabilities including knowledge&comprehension, application, analysis, evaluation, and creation and achieves state-of-the-art performance in multiple social media tasks.
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- Detecting Adversarial Samples through Sharpness of Loss Landscape14
This work proposes a simple and effective sharpness-based detector to distinct adversarial samples by maximizing the loss increment within the region where the inference sample is located and proposes an adaptive optimization strategy in an attempt to fairly compare the relative sharpness among different samples.
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- Constructing Phrase-level Semantic Labels to Form Multi-Grained Supervision for Image-Text Retrieval13
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- Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication13
This paper proposes a new way for visual storytelling by introducing a topic description task to detect the global semantic context of an image stream and proposes a multi-agent communication framework that regards the topic description generator and the story generator as two agents and learn them simultaneously via iterative updating mechanism.
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- Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering11
This paper proposes an interpretable stepwise reasoning framework to incorporate both single-hop supporting sentence identification and single-Hop question generation at each intermediate step, and utilize the inference of the current hop for the next until reasoning out the final result.
- Overview of SMP-CAIL2020-Argmine: The Interactive Argument-Pair Extraction in Judgement Document Challenge11
The results of the Interactive Argument-Pair Extraction in Judgement Document Challenge held by both the Chinese AI and Law Challenge (CAIL) and the Chinese National Social Media Processing Conference (SMP) are presented, and the related data set - SMP-CAIL2020-Argmine is introduced.
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- PIORS: Personalized Intelligent Outpatient Reception based on Large Language Model with Multi-Agents Medical Scenario Simulation10
This paper presents the Personalized Intelligent Outpatient Reception System (PIORS), a system that integrates an LLM-based reception nurse and a collaboration between LLM and hospital information system (HIS) into real outpatient reception setting, aiming to deliver personalized, high-quality, and efficient reception services.
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- UPPAM: A Unified Pre-training Architecture for Political Actor Modeling based on Language9
In UPPAM, aggregate statements to represent political actors and learn the mapping from languages to representation, instead of learning the representation of particular persons, to inject multidimensional information in the political context into the mapping.
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- CauESC: A Causal Aware Model for Emotional Support Conversation7
A novel framework CauESC is proposed, which firstly recognizes the emotion causes of the distress, as well as the emotion effects triggered by the causes, and then understands each strategy of verbal grooming independently and integrates them skillfully.
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- Incorporating Topic Aspects for Online Comment Convincingness Evaluation7
Experimental results show that topic information is able to improve the performance for convincingness evaluation and the model makes a move to detect topic aspects automatically.
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- A Unified Continuous Learning Framework for Multi-modal Knowledge Discovery and Pre-training6
Taking the open-domain uni-modal datasets of images and texts as input, a knowledge graph is maintained as the foundation to support knowledge discovery and model pre-training in a continuous learning framework for mutual improvement.
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- Human vs. LMMs: Exploring the Discrepancy in Emoji Interpretation and Usage in Digital Communication5
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- An Unsupervised Sampling Approach for Image-Sentence Matching Using Document-level Structural Information5
This paper proposes a Transformer based model to capture fine-grained features and implicitly construct a graph for each document, where concepts in a document are introduced to bridge the representation learning of images and sentences in the context of a document.
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- AI-Press: A Multi-Agent News Generating and Feedback Simulation System Powered by Large Language Models4
AI-Press, an automated news drafting and polishing system based on multi-agent collaboration and Retrieval-Augmented Generation is introduced and a feedback simulation system that generates public feedback considering demographic distributions is developed.
- K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via Prompt Learning4
K-ESConv is proposed, a novel prompt learning based knowledge injection method for emotional support dialogue system, transferring forum knowledge to response generation, which outperforms existing baselines on both automatic evaluation and human evaluation.
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- KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition3
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- Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection3
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- Connectivity Patterns are Task Embeddings–
This work draws inspiration from the operating mechanism of deep neural networks (DNNs) and biological brains, where neuronal activations are sparse and task-specific, and uses the connectivity patterns of neurons as a unique identifier associated with the task.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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