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Found 14 papers, 4 papers with code
Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification
Graph representation learning is a fundamental research issue in various domains of applications, of which the inductive learning problem is particularly challenging as it requires models to generalize to unseen graph structures during inference.
YAYI-UIE: A Chat-Enhanced Instruction Tuning Framework for Universal Information Extraction
The difficulty of the information extraction task lies in dealing with the task-specific label schemas and heterogeneous data structures.
YAYI 2: Multilingual Open-Source Large Language Models
As the latest advancements in natural language processing, large language models (LLMs) have achieved human-level language understanding and generation abilities in many real-world tasks, and even have been regarded as a potential path to the artificial general intelligence.
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning
To overcome the limitations of existing unsupervised methods, in this paper, we propose the Isomorphic-Consistent VGAE (IsoC-VGAE) for multi-level task-agnostic graph representation learning.
Disentangled Text Representation Learning with Information-Theoretic Perspective for Adversarial Robustness
Specifically, inspired by the variation of information (VI) in information theory, we derive a disentangled learning objective composed of mutual information to represent both the semantic representativeness of latent embeddings and differentiation of robust and non-robust features.
Spiking Variational Graph Auto-Encoders for Efficient Graph Representation Learning
Graph representation learning is a fundamental research issue and benefits a wide range of applications on graph-structured data.
A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval
Cross-Modal Retrieval (CMR) is an important research topic across multimodal computing and information retrieval, which takes one type of data as the query to retrieve relevant data of another type.
A Deep Latent Space Model for Directed Graph Representation Learning
We propose a Deep Latent Space Model (DLSM) for directed graphs to incorporate the traditional latent space random graph model into deep learning frameworks via a hierarchical variational auto-encoder architecture.
AnANet: Modeling Association and Alignment for Cross-modal Correlation Classification
Thus researchers study the definition of cross-modal correlation category and construct various classification systems and predictive models.
A Deep Latent Space Model for Graph Representation Learning
Our proposed model consists of a graph convolutional network (GCN) encoder and a stochastic decoder, which are layer-wise connected by a hierarchical variational auto-encoder architecture.
A Knowledge Enhanced Learning and Semantic Composition Model for Multi-Claim Fact Checking
Our method consists of two modules, KG-based learning enhancement and multi-claim semantic composition.
Reasoning with Multimodal Sarcastic Tweets via Modeling Cross-Modality Contrast and Semantic Association
In multimodal context, sarcasm is no longer a pure linguistic phenomenon, and due to the nature of social media short text, the opposite is more often manifested via cross-modality expressions.
Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction
Emotion-cause pair extraction aims to extract all emotion clauses coupled with their cause clauses from a given document.
Ranked #1 on
Emotion-Cause Pair Extraction
on ECPE-FanSplit
Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity
The bottom component of our framework classifies the stances of tweets in a conversation discussing a rumor via modeling the structural property based on a novel graph convolutional network.
