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Found 6 papers, 5 papers with code
Multi-task Active Learning for Pre-trained Transformer-based Models
Multi-task learning, in which several tasks are jointly learned by a single model, allows NLP models to share information from multiple annotations and may facilitate better predictions when the tasks are inter-related.
Learning Discrete Structured Variational Auto-Encoder using Natural Evolution Strategies
Discrete variational auto-encoders (VAEs) are able to represent semantic latent spaces in generative learning.
Designing an Automatic Agent for Repeated Language based Persuasion Games
Persuasion games are fundamental in economics and AI research and serve as the basis for important applications.
Model Compression for Domain Adaptation through Causal Effect Estimation
Recent improvements in the predictive quality of natural language processing systems are often dependent on a substantial increase in the number of model parameters.
Deep Contextualized Self-training for Low Resource Dependency Parsing
Neural dependency parsing has proven very effective, achieving state-of-the-art results on numerous domains and languages.
Bridging Languages through Images with Deep Partial Canonical Correlation Analysis
We present a deep neural network that leverages images to improve bilingual text embeddings.
