  Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting triplets
of aspect terms, their associated sentiments, and the opinion terms that
provide evidence for the expressed sentiments. Previous approaches to ASTE
usually simultaneously extract all three components or first identify the
aspect and opinion terms, then pair them up to predict their sentiment
polarities. In this work, we present a novel paradigm, ASTE-RL, by regarding
the aspect and opinion terms as arguments of the expressed sentiment in a
hierarchical reinforcement learning (RL) framework. We first focus on
sentiments expressed in a sentence, then identify the target aspect and opinion
terms for that sentiment. This takes into account the mutual interactions among
the triplet's components while improving exploration and sample efficiency.
Furthermore, this hierarchical RLsetup enables us to deal with multiple and
overlapping triplets. In our experiments, we evaluate our model on existing
datasets from laptop and restaurant domains and show that it achieves
state-of-the-art performance. The implementation of this work is publicly
available at https://github.com/declare-lab/ASTE-RL.
