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Found 6 papers, 3 papers with code
A Unified Approach to Entity-Centric Context Tracking in Social Conversations
Second, we open-source a neural network architecture for context tracking.
Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques
To respond to disasters such as earthquakes, wildfires, and armed conflicts, humanitarian organizations require accurate and timely data in the form of damage assessments, which indicate what buildings and population centers have been most affected.
Schema-Guided Dialogue State Tracking Task at DSTC8
The goal of this task is to develop dialogue state tracking models suitable for large-scale virtual assistants, with a focus on data-efficient joint modeling across domains and zero-shot generalization to new APIs.
Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks
In all types of disasters, from earthquakes to armed conflicts, aid workers need accurate and timely data such as damage to buildings and population displacement to mount an effective response.
Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset
In this work, we introduce the the Schema-Guided Dialogue (SGD) dataset, containing over 16k multi-domain conversations spanning 16 domains.
Neural Input Search for Large Scale Recommendation Models
During training we use reinforcement learning to find the optimal vocabulary size for each feature and embedding dimension for each value of the feature.
