  In this work, we explore the constructive side of online reviews: advice,
tips, requests, and suggestions that users provide about goods, venues,
services, and other items of interest. To reduce training costs and annotation
efforts needed to build a classifier for a specific label set, we present and
evaluate several entailment-based zero-shot approaches to suggestion
classification in a label-fully-unseen fashion. In particular, we introduce the
strategy of assigning target class labels to sentences in English language with
user intentions, which significantly improves prediction quality. The proposed
strategies are evaluated with a comprehensive experimental study that validated
our results both quantitatively and qualitatively.
