  Modeling persuasive language has the potential to better facilitate our
decision-making processes. Despite its importance, computational modeling of
persuasion is still in its infancy, largely due to the lack of benchmark
datasets that can provide quantitative labels of persuasive strategies to
expedite this line of research. To this end, we introduce a large-scale
multi-domain text corpus for modeling persuasive strategies in good-faith text
requests. Moreover, we design a hierarchical weakly-supervised latent variable
model that can leverage partially labeled data to predict such associated
persuasive strategies for each sentence, where the supervision comes from both
the overall document-level labels and very limited sentence-level labels.
Experimental results showed that our proposed method outperformed existing
semi-supervised baselines significantly. We have publicly released our code at
https://github.com/GT-SALT/Persuasion_Strategy_WVAE.
