Open-domain conversation systems integrate multiple conversation skills into
a single system through a modular approach. One of the limitations of the
system, however, is the absence of management capability for external memory.
In this paper, we propose a simple method to improve BlenderBot3 by integrating
memory management ability into it. Since no training data exists for this
purpose, we propose an automating dataset creation for memory management. Our
method 1) requires little cost for data construction, 2) does not affect
performance in other tasks, and 3) reduces external memory. We show that our
proposed model BlenderBot3-M^3, which is multi-task trained with memory
management, outperforms BlenderBot3 with a relative 4% performance gain in
terms of F1 score.

该研究提出了一种简单的方法，通过将记忆管理能力集成到 BlenderBot3 中，并使用自动数据集创建方法来改进 open-domain conversation systems。实验结果表明，多任务培训的 BlenderBot3-M^3 相对于 BlenderBot3 在 F1 得分方面提高了 4%。