Search Results for author:
Found 17 papers, 3 papers with code
Dual Adversarial Alignment for Realistic Support-Query Shift Few-shot Learning
The key feature of RSQS is that the individual samples in a meta-task are subjected to multiple distribution shifts in each meta-task.
Filter Pruning via Filters Similarity in Consecutive Layers
Filter pruning is widely adopted to compress and accelerate the Convolutional Neural Networks (CNNs), but most previous works ignore the relationship between filters and channels in different layers.
STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction
Hence, we propose a novel approach, Span TAgging and Greedy infErence (STAGE), to extract sentiment triplets in span-level, where each span may consist of multiple words and play different roles simultaneously.
HCL: Improving Graph Representation with Hierarchical Contrastive Learning
Contrastive learning has emerged as a powerful tool for graph representation learning.
HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold
Conventional event detection models under supervised learning settings suffer from the inability of transfer to newly-emerged event types owing to lack of sufficient annotations.
Multi-level Contrastive Learning Framework for Sequential Recommendation
Different from the previous contrastive learning-based methods for SR, MCLSR learns the representations of users and items through a cross-view contrastive learning paradigm from four specific views at two different levels (i. e., interest- and feature-level).
Improving Personality Consistency in Conversation by Persona Extending
Endowing chatbots with a consistent personality plays a vital role for agents to deliver human-like interactions.
Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning
Specifically, we first construct local and non-local graphs for user/item in KG, exploring more KG facts for KGR.
PASH at TREC 2021 Deep Learning Track: Generative Enhanced Model for Multi-stage Ranking
This paper describes the PASH participation in TREC 2021 Deep Learning Track.
Visual-Semantic Transformer for Scene Text Recognition
In this work, we propose to model semantic and visual information jointly with a Visual-Semantic Transformer (VST).
funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts
This paper describes the approach we used for SemEval-2017 Task 4: Sentiment Analysis in Twitter.
funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter
This paper describes the approach we used for SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs.
