  Aspect sentiment quad prediction (ASQP) aims to predict four aspect-based
elements, including aspect term, opinion term, aspect category, and sentiment
polarity. In practice, unseen aspects, due to distinct data distribution,
impose many challenges for a trained neural model. Motivated by this, this work
formulates ASQP into the few-shot scenario, which aims for fast adaptation in
real applications. Therefore, we first construct a few-shot ASQP dataset (FSQP)
that contains richer categories and is more balanced for the few-shot study.
Moreover, recent methods extract quads through a generation paradigm, which
involves converting the input sentence into a templated target sequence.
However, they primarily focus on the utilization of a single template or the
consideration of different template orders, thereby overlooking the
correlations among various templates. To tackle this issue, we further propose
a Broadview Soft Prompting (BvSP) method that aggregates multiple templates
with a broader view by taking into account the correlation between the
different templates. Specifically, BvSP uses the pre-trained language model to
select the most relevant k templates with Jensen-Shannon divergence. BvSP
further introduces soft prompts to guide the pre-trained language model using
the selected templates. Then, we aggregate the results of multi-templates by
voting mechanism. Empirical results demonstrate that BvSP significantly
outperforms the stateof-the-art methods under four few-shot settings and other
public datasets. Our code and dataset are available at
https://github.com/byinhao/BvSP.
