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    <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.</abstract>
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%0 Conference Proceedings
%T Target-to-Source Augmentation for Aspect Sentiment Triplet Extraction
%A Zhang, Yice
%A Yang, Yifan
%A Li, Meng
%A Liang, Bin
%A Chen, Shiwei
%A Xu, Ruifeng
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhang-etal-2023-target-source
%X 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.
%R 10.18653/v1/2023.emnlp-main.747
%U https://aclanthology.org/2023.emnlp-main.747
%U https://doi.org/10.18653/v1/2023.emnlp-main.747
%P 12165-12177

Markdown (Informal)

[Target-to-Source Augmentation for Aspect Sentiment Triplet Extraction](https://aclanthology.org/2023.emnlp-main.747) (Zhang et al., EMNLP 2023)

• Target-to-Source Augmentation for Aspect Sentiment Triplet Extraction (Zhang et al., EMNLP 2023)

ACL

• Yice Zhang, Yifan Yang, Meng Li, Bin Liang, Shiwei Chen, and Ruifeng Xu. 2023. Target-to-Source Augmentation for Aspect Sentiment Triplet Extraction. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12165–12177, Singapore. Association for Computational Linguistics.