What techniques improve multi-turn dialogue coherence in conversational AI using transformers

Question

Can you name the techniques to improve multi-turn dialogue coherence in conversational AI using transformers?

Answer 1

In order to improve multi-turn dialogue coherence in conversational AI using transformers, you can refer to the techniques below:

• Context Window Expansion: It concatenates previous turns in the input to maintain context.
• Memory Networks: It uses a memory mechanism to store and retrieve relevant dialogue history.
• Fine-Tuning on Dialogue Data: It helps in Training the model on high-quality multi-turn dialogue datasets.
• Persona Integration: It incorporates user and bot personas for consistency.
• Reinforcement Learning (RLHF): Fine-tune responses using human feedback or reward models.

Here is the code snippet you can refer to:

In the above code, we are using Expanded Context to incorporate history into inputs to improve coherence, Fine-tuning to train on domain-specific, multi-turn data to boost relevance, and RLHF to optimize for logical, human-like responses.

Hence, these techniques will improve multi-turn dialogue coherence in conversational AI using transformers.