End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and
  Future Directions
Libo Qin Wenbo Pan Qiguang Chen Lizi Liao Zhou Yu Yue Zhang Wanxiang Che Min Li
End-to-end task-oriented dialogue (EToD) can directly generate responses in
an end-to-end fashion without modular training, which attracts escalating
popularity. The advancement of deep neural networks, especially the successful
use of large pre-trained models, has further led to significant progress in
EToD research in recent years. In this paper, we present a thorough review and
provide a unified perspective to summarize existing approaches as well as
recent trends to advance the development of EToD research. The contributions of
this paper can be summarized: (1) \textbf{\textit{First survey}}: to our
knowledge, we take the first step to present a thorough survey of this research
field; (2) \textbf{\textit{New taxonomy}}: we first introduce a unified
perspective for EToD, including (i) \textit{Modularly EToD} and (ii)
\textit{Fully EToD}; (3) \textbf{\textit{New Frontiers}}: we discuss some
potential frontier areas as well as the corresponding challenges, hoping to
spur breakthrough research in EToD field; (4) \textbf{\textit{Abundant
resources}}: we build a public website\footnote{We collect the related papers,
baseline projects, and leaderboards for the community at
\url{https://etods.net/}.}, where EToD researchers could directly access the
recent progress. We hope this work can serve as a thorough reference for the
EToD research community.