  Recently, aspect sentiment quad prediction has received widespread attention
in the field of aspect-based sentiment analysis. Existing studies extract
quadruplets via pre-trained generative language models to paraphrase the
original sentence into a templated target sequence. However, previous works
only focus on what to generate but ignore what not to generate. We argue that
considering the negative samples also leads to potential benefits. In this
work, we propose a template-agnostic method to control the token-level
generation, which boosts original learning and reduces mistakes simultaneously.
Specifically, we introduce Monte Carlo dropout to understand the built-in
uncertainty of pre-trained language models, acquiring the noises and errors. We
further propose marginalized unlikelihood learning to suppress the
uncertainty-aware mistake tokens. Finally, we introduce minimization entropy to
balance the effects of marginalized unlikelihood learning. Extensive
experiments on four public datasets demonstrate the effectiveness of our
approach on various generation templates.
