  Understanding the reason for emotional support response is crucial for
establishing connections between users and emotional support dialogue systems.
Previous works mostly focus on generating better responses but ignore
interpretability, which is extremely important for constructing reliable
dialogue systems. To empower the system with better interpretability, we
propose an emotional support response generation scheme, named
$\textbf{E}$motion-Focused and $\textbf{S}$trategy-Driven
$\textbf{C}$hain-$\textbf{o}$f-$\textbf{T}$hought ($\textbf{ESCoT}$), mimicking
the process of $\textit{identifying}$, $\textit{understanding}$, and
$\textit{regulating}$ emotions. Specially, we construct a new dataset with
ESCoT in two steps: (1) $\textit{Dialogue Generation}$ where we first generate
diverse conversation situations, then enhance dialogue generation using richer
emotional support strategies based on these situations; (2) $\textit{Chain
Supplement}$ where we focus on supplementing selected dialogues with elements
such as emotion, stimuli, appraisal, and strategy reason, forming the manually
verified chains. Additionally, we further develop a model to generate dialogue
responses with better interpretability. We also conduct extensive experiments
and human evaluations to validate the effectiveness of the proposed ESCoT and
generated dialogue responses. Our data and code are available at
$\href{https://github.com/TeigenZhang/ESCoT}{https://github.com/TeigenZhang/ESCoT}$.
