UniMC: A Unified Framework for Long-Term Memory Conversation via
  Relevance Representation Learning
Kang Zhao Wei Liu Jian Luan Minglei Gao Li Qian Hanlin Teng Bin Wang
Open-domain long-term memory conversation can establish long-term intimacy
with humans, and the key is the ability to understand and memorize long-term
dialogue history information. Existing works integrate multiple models for
modelling through a pipeline, which ignores the coupling between different
stages. In this paper, we propose a Unified framework for Long-term Memory
Conversations (UniMC), which increases the connection between different stages
by learning relevance representation. Specifically, we decompose the main task
into three subtasks based on probability graphs: 1) conversation summarization,
2) memory retrieval, 3) memory-augmented generation. Each subtask involves
learning a representation for calculating the relevance between the query and
memory, which is modelled by inserting a special token at the beginning of the
decoder input. The relevance representation learning strengthens the connection
across subtasks through parameter sharing and joint training. Extensive
experimental results show that the proposed method consistently improves over
strong baselines and yields better dialogue consistency and engagingness.