Search Results for author:
Found 15 papers, 4 papers with code
LEGO-ABSA: A Prompt-based Task Assemblable Unified Generative Framework for Multi-task Aspect-based Sentiment Analysis
This paper proposes a unified generative multi-task framework that can solve multiple ABSA tasks by controlling the type of task prompts consisting of multiple element prompts.
Ranked #5 on
Aspect-Based Sentiment Analysis (ABSA)
on TASD
(using extra training data)
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels
In this paper, we advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior about the label-to-label correspondence via a semantic prior prompter.
Learning Multi-Stage Multi-Grained Semantic Embeddings for E-Commerce Search
Retrieving relevant items that match users' queries from billion-scale corpus forms the core of industrial e-commerce search systems, in which embedding-based retrieval (EBR) methods are prevailing.
Adaptive Experimentation with Delayed Binary Feedback
Experiments show that the proposed method is more efficient for delayed feedback compared to various other approaches and is robust in different settings.
Blending Advertising with Organic Content in E-Commerce: A Virtual Bids Optimization Approach
In e-commerce platforms, sponsored and non-sponsored content are jointly displayed to users and both may interactively influence their engagement behavior.
Probing Product Description Generation via Posterior Distillation
To tackle this problem, we propose an adaptive posterior network based on Transformer architecture that can utilize user-cared information from customer reviews.
DADNN: Multi-Scene CTR Prediction via Domain-Aware Deep Neural Network
More importantly, DADNN utilizes a single model for multiple scenes which saves a lot of offline training and online serving resources.
BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search
Relevance has significant impact on user experience and business profit for e-commerce search platform.
Kalman Filtering Attention for User Behavior Modeling in CTR Prediction
First, conventional attentions mostly limit the attention field only to a single user's behaviors, which is not suitable in e-commerce where users often hunt for new demands that are irrelevant to any historical behaviors.
Group-wise Contrastive Learning for Neural Dialogue Generation
Neural dialogue response generation has gained much popularity in recent years.
Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint
In addition, feature importance for the purpose of CTR/CVR predictions differs from one category to another.
Category-Specific CNN for Visual-aware CTR Prediction at JD.com
Existing algorithms usually extract visual features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse the visual and non-visual features for the finally predicted CTR.
Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning
Two critical challenges stay in today's e-commerce search: how to retrieve items that are semantically relevant but not exact matching to query terms, and how to retrieve items that are more personalized to different users for the same search query.
Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction
In deep CTR models, exploiting users' historical data is essential for learning users' behaviors and interests.
Deep Hierarchical Reinforcement Learning Based Recommendations via Multi-goals Abstraction
To tackle this challenge, we propose a deep hierarchical reinforcement learning based recommendation framework, which consists of two components, i. e., high-level agent and low-level agent.
Hierarchical Reinforcement Learning
Recommendation Systems
+2
