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Found 12 papers, 1 papers with code
Few-Shot Structured Policy Learning for Multi-Domain and Multi-Task Dialogues
Reinforcement learning has been widely adopted to model dialogue managers in task-oriented dialogues.
Graph Neural Network Policies and Imitation Learning for Multi-Domain Task-Oriented Dialogues
Task-oriented dialogue systems are designed to achieve specific goals while conversing with humans.
Neural-Driven Search-Based Paraphrase Generation
We study a search-based paraphrase generation scheme where candidate paraphrases are generated by iterated transformations from the original sentence and evaluated in terms of syntax quality, semantic distance, and lexical distance.
Denoising Pre-Training and Data Augmentation Strategies for Enhanced RDF Verbalization with Transformers
The task of verbalization of RDF triples has known a growth in popularity due to the rising ubiquity of Knowledge Bases (KBs).
Diluted Near-Optimal Expert Demonstrations for Guiding Dialogue Stochastic Policy Optimisation
We notably propose a randomised exploration policy which allows for a seamless hybridisation of the learned policy and the expert.
Neural-Driven Multi-criteria Tree Search for Paraphrase Generation
A good paraphrase is semantically similar to the original sentence but it must be also well formed, and syntactically different to ensure diversity.
Budgeted Reinforcement Learning in Continuous State Space
A Budgeted Markov Decision Process (BMDP) is an extension of a Markov Decision Process to critical applications requiring safety constraints.
Corrupt Bandits for Preserving Local Privacy
In this framework, motivated by privacy preservation in online recommender systems, the goal is to maximize the sum of the (unobserved) rewards, based on the observation of transformation of these rewards through a stochastic corruption process with known parameters.
Bandit Structured Prediction for Learning from Partial Feedback in Statistical Machine Translation
We present an application to discriminative reranking in Statistical Machine Translation (SMT) where the learning algorithm only has access to a 1-BLEU loss evaluation of a predicted translation instead of obtaining a gold standard reference translation.
A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits
We study the K-armed dueling bandit problem which is a variation of the classical Multi-Armed Bandit (MAB) problem in which the learner receives only relative feedback about the selected pairs of arms.
Utility-based Dueling Bandits as a Partial Monitoring Game
Partial monitoring is a generic framework for sequential decision-making with incomplete feedback.
Random Forest for the Contextual Bandit Problem - extended version
The dependence of the sample complexity upon the number of contextual variables is logarithmic.
