Search Results for author:
Found 5 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.
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.
Driver Action Prediction Using Deep (Bidirectional) Recurrent Neural Network
In this paper, we formulate driver action prediction as a timeseries anomaly prediction problem.
