Target-to-Source Augmentation for Aspect Sentiment Triplet Extraction (2023)

Abstract: Aspect Sentiment Triplet Extraction (ASTE) is an important task in sentiment analysis, aiming to extract aspect-level opinions and sentiments from user-generated reviews. The fine-grained nature of ASTE incurs a high annotation cost, while the scarcity of annotated data limits the performance of existing methods. This paper exploits data augmentation to address this issue. Traditional augmentation methods typically modify the input sentences of existing samples via heuristic rules or language models, which have shown success in text classification tasks. However, applying these methods to fine-grained tasks like ASTE poses challenges in generating diverse augmented samples while maintaining alignment between modified sentences and origin labels. Therefore, this paper proposes a target-to-source augmentation approach for ASTE. Our approach focuses on learning a generator that can directly generate new sentences based on labels and syntactic templates. With this generator, we can generate a substantial number of diverse augmented samples by mixing labels and syntactic templates from different samples. Besides, to ensure the quality of the generated sentence, we introduce fluency and alignment discriminators to provide feedback on the generated sentence and then use this feedback to optimize the generator via a reinforcement learning framework. Experiments demonstrate that our approach significantly enhances the performance of existing ASTE models.

## introduction aspect sentiment triplet extraction (aste) is an important task in sentiment analysis @xcite , which is receiving increasing attention in original sentence and label traditional augmentation modifies the original sentence and combines it with the original label to form an augmented sample. ## problem definition target-to-source augmentation aims to learn a generator that can generate corresponding sentences based on given labels and syntactic templates. in this paper, we use the dependency tree as the syntactic template. formally, the generator is defined as follows: we have three requirements for the augmented sample: ## conclusion in this paper, we propose a target-to-source augmentation approach to alleviate the issue of data scarcity in aspect sentiment triplet extraction (aste). unlike traditional augmentation methods that modify input sentences, the target-to-source approach focuses on learning a generator to directly generate new sentences based on labels and syntactic templates. by combining the labels and syntactic templates from different samples, this generator can generate a substantial number of diverse augmented samples. to train the generator, we employ a reinforcement learning framework. specifically, we introduce a fluency discriminator and an alignment discriminator to provide feedback for the generated sentences. then, we utilize the proximal policy optimization (ppo) algorithm to optimize the generator based on this feedback. we apply the proposed augmentation approach to public aste datasets and evaluate the augmented datasets using four representative aste models. the experimental results show that the proposed