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Found 7 papers, 1 papers with code
Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards
Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward function.
Ensemble-Based Deep Reinforcement Learning for Chatbots
Experimental results using chitchat data reveal that (1) near human-like dialogue policies can be induced, (2) generalisation to unseen data is a difficult problem, and (3) training an ensemble of chatbot agents is essential for improved performance over using a single agent.
A Study on Dialogue Reward Prediction for Open-Ended Conversational Agents
The amount of dialogue history to include in a conversational agent is often underestimated and/or set in an empirical and thus possibly naive way.
Out-of-domain Detection based on Generative Adversarial Network
The main goal of this paper is to develop out-of-domain (OOD) detection for dialog systems.
Generative Adversarial Network
Out of Distribution (OOD) Detection
+1
Neural Sentence Embedding using Only In-domain Sentences for Out-of-domain Sentence Detection in Dialog Systems
Then we used domain-category analysis as an auxiliary task to train neural sentence embedding for OOD sentence detection.
