Learning to mirror speaking styles incrementally
This work addresses the need for more efficient persona adaptation in chatbots, though it is incremental as it builds on existing style-shifting methods.
The paper tackles the problem of chatbots requiring large datasets to adopt specific speaking styles by proposing a method that incrementally learns to mirror a person's speaking style using ngrams to transform sentences, with experiments showing it can capture and apply these patterns.
Mirroring is the behavior in which one person subconsciously imitates the gesture, speech pattern, or attitude of another. In conversations, mirroring often signals the speakers enjoyment and engagement in their communication. In chatbots, methods have been proposed to add personas to the chatbots and to train them to speak or to shift their dialogue style to that of the personas. However, they often require a large dataset consisting of dialogues of the target personalities to train. In this work, we explore a method that can learn to mirror the speaking styles of a person incrementally. Our method extracts ngrams that capture a persons speaking styles and uses the ngrams to create patterns for transforming sentences to the persons speaking styles. Our experiments show that our method is able to capture patterns of speaking style that can be used to transform regular sentences into sentences with the target style.