A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

Understand

We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations.

• A neural network architecture is used to address sparsity issues that arise when integrating contextual information into classic statistical models, allowing the system to take into account previous dialog utterances.
• Our dynamic-context generative models show consistent gains over both context-sensitive and non-context-sensitive Machine Translation and Information Retrieval baselines.