  Metaphors play a significant role in our everyday communication, yet
detecting them presents a challenge. Traditional methods often struggle with
improper application of language rules and a tendency to overlook data
sparsity. To address these issues, we integrate knowledge distillation and
prompt learning into metaphor detection. Our approach revolves around a
tailored prompt learning framework specifically designed for metaphor
detection. By strategically masking target words and providing relevant prompt
data, we guide the model to accurately predict the contextual meanings of these
words. This approach not only mitigates confusion stemming from the literal
meanings of the words but also ensures effective application of language rules
for metaphor detection. Furthermore, we've introduced a teacher model to
generate valuable soft labels. These soft labels provide a similar effect to
label smoothing and help prevent the model from becoming over confident and
effectively addresses the challenge of data sparsity. Experimental results
demonstrate that our model has achieved state-of-the-art performance, as
evidenced by its remarkable results across various datasets.
