Effortless Integration of Memory Management into Open-Domain
  Conversation Systems
Eunbi Choi Kyoung-Woon On Gunsoo Han Sungwoong Kim Daniel Wontae Nam Daejin Jo Seung Eun Rho Taehwan Kwon Minjoon Seo
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.