The goal of building intelligent dialogue systems has largely been separately
pursued under two paradigms: task-oriented dialogue (TOD) systems, which
perform goal-oriented functions, and open-domain dialogue (ODD) systems, which
focus on non-goal-oriented chitchat. The two dialogue modes can potentially be
intertwined together seamlessly in the same conversation, as easily done by a
friendly human assistant. Such ability is desirable in conversational agents,
as the integration makes them more accessible and useful. Our paper addresses
this problem of fusing TODs and ODDs in multi-turn dialogues. Based on the
popular TOD dataset MultiWOZ, we build a new dataset FusedChat, by rewriting
the existing TOD turns and adding new ODD turns. This procedure constructs
conversation sessions containing exchanges from both dialogue modes. It
features inter-mode contextual dependency, i.e., the dialogue turns from the
two modes depend on each other. Rich dependency patterns including co-reference
and ellipsis are features. The new dataset, with 60k new human-written ODD
turns and 5k re-written TOD turns, offers a benchmark to test a dialogue
model's ability to perform inter-mode conversations. This is a more challenging
task since the model has to determine the appropriate dialogue mode and
generate the response based on the inter-mode context. But such models would
better mimic human-level conversation capabilities. We evaluate baseline models
on this task, including classification-based two-stage models and two-in-one
fused models. We publicly release FusedChat and the baselines to propel future
work on inter-mode dialogue systems this https URL

本文介绍了把目标导向式对话任务（TOD）系统和非目标导向闲聊式对话系统（ODD）结合起来的难题，并基于流行的 TOD 数据集 MultiWOZ 构建一个包含两种对话模式交替的新数据集 FusedChat，提供一种更具挑战性的基准测试，以测试对话模型在处理交叉模式上的表现。该数据集具有丰富的依赖模式，包括共示和省略，并提供了分类和融合等基线模型。