Search Results for author:
Found 16 papers, 9 papers with code
DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations
Emotion Recognition in Conversations (ERC) has gained increasing attention for developing empathetic machines.
Ranked #14 on
Emotion Recognition in Conversation
on EmoryNLP
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.
Ranked #23 on
Emotion Recognition in Conversation
on IEMOCAP
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.
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 #4 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
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
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.
Structured Probabilistic Coding
It can enhance the generalization ability of pre-trained language models for better language understanding.
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
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.
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.
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.
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.
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.
Can Large Language Models Understand Content and Propagation for Misinformation Detection: An Empirical Study
In this paper, we present a comprehensive empirical study to explore the performance of LLMs on misinformation detection tasks.
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.
