Aspect Sentiment Triplet Extraction Using Reinforcement Learning

TL;DR

This paper introduces ASTE-RL, a hierarchical reinforcement learning approach for aspect sentiment triplet extraction that effectively captures interactions among components and handles multiple overlapping triplets, achieving state-of-the-art results.

Contribution

The paper proposes a novel hierarchical reinforcement learning framework for ASTE that improves extraction accuracy and handles complex triplet structures more effectively than previous methods.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Effectively handles multiple and overlapping triplets.

Improves exploration and sample efficiency in ASTE tasks.

Abstract

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.