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Found 19 papers, 11 papers with code
Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion Cause Pair Extraction
Besides, a clause graph is also established to model coarse-grained semantic relations between clauses.
A Unified Propagation Forest-based Framework for Fake News Detection
Fake news’s quick propagation on social media brings severe social ramifications and economic damage.
Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection
In this paper, we propose a novel dual graph-based model, Uncertainty-aware Propagation Structure Reconstruction (UPSR) for improving fake news detection.
An Information-theoretic Multi-task Representation Learning Framework for Natural Language Understanding
Firstly, a shared information maximization principle is proposed to learn more sufficient shared representations for all target tasks.
Transferring Structure Knowledge: A New Task to Fake news Detection Towards Cold-Start Propagation
To achieve the task, we design a simple but effective Structure Adversarial Net (SAN) framework to learn transferable features from available propagation to boost the detection of content-only samples.
Representation Learning with Conditional Information Flow Maximization
This paper proposes an information-theoretic representation learning framework, named conditional information flow maximization, to extract noise-invariant sufficient representations for the input data and target task.
Structured Probabilistic Coding
It can enhance the generalization ability of pre-trained language models for better language understanding.
Are Large Language Models Good Fact Checkers: A Preliminary Study
However, they encounter challenges in effectively handling Chinese fact verification and the entirety of the fact-checking pipeline due to language inconsistencies and hallucinations.
Explore the Potential of LLMs in Misinformation Detection: An Empirical Study
Our empirical studies on eight misinformation detection datasets show that LLM-based detectors can achieve comparable performance in text-based misinformation detection but exhibit notably constrained capabilities in comprehending propagation structure compared to existing models in propagation-based misinformation detection.
Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations
To address this, we propose a supervised adversarial contrastive learning (SACL) framework for learning class-spread structured representations in a supervised manner.
Ranked #5 on
Emotion Recognition in Conversation
on EmoryNLP
UCAS-IIE-NLP at SemEval-2023 Task 12: Enhancing Generalization of Multilingual BERT for Low-resource Sentiment Analysis
To alleviate these, we propose a generalized multilingual system SACL-XLMR for sentiment analysis on low-resource languages.
Ranked #1 on
Zero-shot Sentiment Classification
on AfriSenti
Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation
Since the dependencies between speakers are complex and dynamic, which consist of intra- and inter-speaker dependencies, the modeling of speaker-specific information is a vital role in ERC.
Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion-Cause Pair Extraction
Besides, a clause graph is also established to model coarse-grained semantic relations between clauses.
MM-DFN: Multimodal Dynamic Fusion Network for Emotion Recognition in Conversations
For multimodal ERC, it is vital to understand context and fuse modality information in conversations.
Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection
Detecting rumors on social media is a very critical task with significant implications to the economy, public health, etc.
PEN4Rec: Preference Evolution Networks for Session-based Recommendation
The process can strengthen the effect of relevant sequential behaviors during the preference evolution and weaken the disturbance from preference drifting.
Ranked #1 on
Session-Based Recommendations
on Last.FM
DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations
Emotion Recognition in Conversations (ERC) has gained increasing attention for developing empathetic machines.
Ranked #5 on
Emotion Recognition in Conversation
on IEMOCAP-4
Hierarchical Interaction Networks with Rethinking Mechanism for Document-level Sentiment Analysis
Furthermore, we design a Sentiment-based Rethinking mechanism (SR) by refining the HIN with sentiment label information to learn a more sentiment-aware document representation.
SLK-NER: Exploiting Second-order Lexicon Knowledge for Chinese NER
Although character-based models using lexicon have achieved promising results for Chinese named entity recognition (NER) task, some lexical words would introduce erroneous information due to wrongly matched words.
Ranked #7 on
Chinese Named Entity Recognition
on Resume NER
Chinese Named Entity Recognition
named-entity-recognition
+3
