  Conversations are always related to certain topics. However, it is
challenging to fuse dialogue history and topic information from various sources
at the same time in current dialogue generation models because of the input
length limit of pre-trained language models (PLMs). In order to expand the
information that PLMs can utilize, we encode topic and dialogue history
information using certain prompts with multiple channels of Fusion-in-Decoder
(FiD) and explore the influence of three different channel settings. In this
paper, our experiments focus on a specific Chinese dataset named NaturalConv,
where the conversation revolves around a piece of recent news. We thoroughly
compared different dialogue models and different FiD channel settings.
Empirical results show that by combining our proposed whole passage channel
with additional history channel, our methods can achieve competitive
performance on NaturalConv, making it possible to encode various information
from excessively long texts.
