Towards Adaptive Context Management for Intelligent Conversational Question Answering
This work addresses the challenge of handling long conversation histories for ConvQA systems, offering a scalable solution that could enhance robustness, though it appears incremental in its approach.
The paper tackles the problem of managing conversation history in Conversational Question Answering (ConvQA) systems by introducing an Adaptive Context Management (ACM) framework, which dynamically optimizes context within token limits to improve response accuracy and contextual appropriateness.
This particular paper introduces an Adaptive Context Management (ACM) framework for the Conversational Question Answering (ConvQA) systems. The key objective of the ACM framework is to optimize the use of the conversation history by dynamically managing context for maximizing the relevant information provided to a ConvQA model within its token limit. Our approach incorporates a Context Manager (CM) Module, a Summarization (SM) Module, and an Entity Extraction (EE) Module in a bid to handle the conversation history efficaciously. The CM Module dynamically adjusts the context size, thereby preserving the most relevant and recent information within a model's token limit. The SM Module summarizes the older parts of the conversation history via a sliding window. When the summarization window exceeds its limit, the EE Module identifies and retains key entities from the oldest conversation turns. Experimental results demonstrate the effectiveness of our envisaged framework in generating accurate and contextually appropriate responses, thereby highlighting the potential of the ACM framework to enhance the robustness and scalability of the ConvQA systems.