  Due to the lack of human resources for mental health support, there is an
increasing demand for employing conversational agents for support. Recent work
has demonstrated the effectiveness of dialogue models in providing emotional
support. As previous studies have demonstrated that seekers' persona is an
important factor for effective support, we investigate whether there are
benefits to modeling such information in dialogue models for support. In this
paper, our empirical analysis verifies that persona has an important impact on
emotional support. Therefore, we propose a framework for dynamically inferring
and modeling seekers' persona. We first train a model for inferring the
seeker's persona from the conversation history. Accordingly, we propose PAL, a
model that leverages persona information and, in conjunction with our
strategy-based controllable generation method, provides personalized emotional
support. Automatic and manual evaluations demonstrate that PAL achieves
state-of-the-art results, outperforming the baselines on the studied benchmark.
Our code and data are publicly available at https://github.com/chengjl19/PAL.
