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Found 11 papers, 3 papers with code
Thresholding Classifiers to Maximize F1 Score
As another special case, if the classifier is completely uninformative, then the optimal behavior is to classify all examples as positive.
A Multi-Horizon Quantile Recurrent Forecaster
We propose a framework for general probabilistic multi-step time series regression.
Achieving Fluency and Coherency in Task-oriented Dialog
We show how to combine nearest neighbor and Seq2Seq methods in a hybrid model, where nearest neighbor is used to generate fluent responses and Seq2Seq type models ensure dialog coherency and generate accurate external actions.
What we need to learn if we want to do and not just talk
In task-oriented dialog, agents need to generate both fluent natural language responses and correct external actions like database queries and updates.
Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog
Neural network models have recently gained traction for sentence-level intent classification and token-based slot-label identification.
ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems
Reinforcement Learning (RL) has achieved state-of-the-art results in domains such as robotics and games.
Recursive Template-based Frame Generation for Task Oriented Dialog
The Natural Language Understanding (NLU) component in task oriented dialog systems processes a user{'}s request and converts it into structured information that can be consumed by downstream components such as the Dialog State Tracker (DST).
Zero-Shot Learning for Joint Intent and Slot Labeling
It is expensive and difficult to obtain the large number of sentence-level intent and token-level slot label annotations required to train neural network (NN)-based Natural Language Understanding (NLU) components of task-oriented dialog systems, especially for the many real world tasks that have a large and growing number of intents and slot types.
Predict-and-Critic: Accelerated End-to-End Predictive Control for Cloud Computing through Reinforcement Learning
PnC jointly trains a prediction model and a terminal Q function that approximates cost-to-go over a long horizon, by back-propagating the cost of decisions through the optimization problem \emph{and from the future}.
