  Within textual emotion classification, the set of relevant labels depends on
the domain and application scenario and might not be known at the time of model
development. This conflicts with the classical paradigm of supervised learning
in which the labels need to be predefined. A solution to obtain a model with a
flexible set of labels is to use the paradigm of zero-shot learning as a
natural language inference task, which in addition adds the advantage of not
needing any labeled training data. This raises the question how to prompt a
natural language inference model for zero-shot learning emotion classification.
Options for prompt formulations include the emotion name anger alone or the
statement "This text expresses anger". With this paper, we analyze how
sensitive a natural language inference-based zero-shot-learning classifier is
to such changes to the prompt under consideration of the corpus: How carefully
does the prompt need to be selected? We perform experiments on an established
set of emotion datasets presenting different language registers according to
different sources (tweets, events, blogs) with three natural language inference
models and show that indeed the choice of a particular prompt formulation needs
to fit to the corpus. We show that this challenge can be tackled with
combinations of multiple prompts. Such ensemble is more robust across corpora
than individual prompts and shows nearly the same performance as the individual
best prompt for a particular corpus.
