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Found 6 papers, 0 papers with code
Adversarial Bootstrapping for Dialogue Model Training
Our experiments shows that adversarial bootstrapping is effective at addressing exposure bias, leading to improvement in response relevance and coherence.
DLGNet: A Transformer-based Model for Dialogue Response Generation
Neural dialogue models, despite their successes, still suffer from lack of relevance, diversity, and in many cases coherence in their generated responses.
A Persona-based Multi-turn Conversation Model in an Adversarial Learning Framework
In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to multi-turn dialogue by modifying the state-of-the-art hredGAN architecture.
An Adversarial Learning Framework For A Persona-Based Multi-Turn Dialogue Model
In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to a multi-turn dialogue scenario by modifying the state-of-the-art hredGAN architecture to simultaneously capture utterance attributes such as speaker identity, dialogue topic, speaker sentiments and so on.
Multi-turn Dialogue Response Generation in an Adversarial Learning Framework
We propose an adversarial learning approach for generating multi-turn dialogue responses.
Using Thought-Provoking Children's Questions to Drive Artificial Intelligence Research
These questions are designed to stimulate thought and learning in children, and they can be used to do the same thing in AI systems, while demonstrating the system's reasoning capabilities to the evaluator.
