Using Synthetic Data for Conversational Response Generation in Low-resource Settings

Understand

Response generation is a task in natural language processing (NLP) where a model is trained to respond to human statements.

• Conversational response generators take this one step further with the ability to respond within the context of previous responses.
• While there are existing techniques for training such models, they all require an abundance of conversational data which are not always available for low-resource languages.
• In this research, we make three contributions.