Aspect Sentiment Triplet Extraction Using Reinforcement Learning
This addresses the problem of extracting detailed sentiment structures from text for applications like review analysis, though it is an incremental improvement over existing methods.
The paper tackles Aspect Sentiment Triplet Extraction (ASTE) by proposing ASTE-RL, a hierarchical reinforcement learning framework that treats sentiments as primary and extracts associated aspect and opinion terms, achieving state-of-the-art performance on laptop and restaurant datasets.
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.