How can I use transformers to improve the performance of dialogue systems by considering contextual memory

Question

With the help of proper code example can you tell me How can I use transformers to improve the performance of dialogue systems by considering contextual memory?

Answer 1

To improve dialogue systems with contextual memory, use transformers with memory-efficient attention mechanisms, conversational history tracking, and long-context embeddings for more coherent and relevant responses.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Sliding Window Context Memory – Uses deque(maxlen=5) to track recent conversational history for coherent responses.
• Transformers with Long-Context Handling – Integrates past dialogue turns into the prompt for context-aware generation.
• Efficient Memory Management – Prevents excessive memory usage by limiting stored exchanges.
• Adaptive Conversations – Allows the chatbot to remember user preferences and maintain logical flow.
• Scalable for Real-Time Use – Can be extended with vector embeddings for deeper contextual memory across long conversations.

Hence, improving dialogue systems with transformers requires context tracking, sliding memory windows, and long-context embeddings to generate more relevant and coherent responses dynamically.