Term-Prompted and Dual-Path Text Generation for Aspect Sentiment Triplet Extraction

Li Yinqiang; Lan Tian; Liu Yao; Xiang Feiyang; Sun Lichun; Du Zhihan; Liu Qiao

doi:10.7544/issn1000-1239.202330838

Journal of Computer Research and Development > 2025 > 62(4): 915-929. > DOI: 10.7544/issn1000-1239.202330838 CSTR: 32373.14.issn1000-1239.202330838

Li Yinqiang, Lan Tian, Liu Yao, Xiang Feiyang, Sun Lichun, Du Zhihan, Liu Qiao. Term-Prompted and Dual-Path Text Generation for Aspect Sentiment Triplet Extraction[J]. Journal of Computer Research and Development, 2025, 62(4): 915-929. DOI: 10.7544/issn1000-1239.202330838

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Term-Prompted and Dual-Path Text Generation for Aspect Sentiment Triplet Extraction

1.
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054
2.
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731

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Author Bio:
Li Yinqiang: born in 1998. Master candidate. His main research interests include aspect-based sentiment analysis and machine learning

Lan Tian: born in 1977. PhD, professor, master supervisor. His main research interests include image processing, speech enhancement, and natural language processing

Liu Yao: born in 1978. PhD, associate professor. Member of CCF. Her main research interests include network security, social network data mining, and machine learning

Xiang Feiyang: born in 1995. Master candidate. His main research interests include continual learning and machine learning

Sun Lichun: born in 1999. Master candidate. Her main research interests include stance detection and machine learning

Du Zhihan: born in 1999. Master candidate. His main research interests include sentiment analysis and machine learning

Liu Qiao: born in 1974. PhD, professor, PhD supervisor. His main research interests include natural language processing, machine learning, and data mining
Received Date: October 30, 2023
Revised Date: June 10, 2024
Accepted Date: August 08, 2024
Available Online: August 16, 2024

Graphical Abstract

Abstract

Abstract

Aspect sentiment triplet extraction (ASTE) is a challenging subtask within aspect-based sentiment analysis. It aims to extract triplets consisting of aspect terms, opinion terms, and sentiment polarities from texts. In the recent past, generative extraction techniques have demonstrated remarkable efficacy through the sequential concatenation of target triplets, thereby enabling the autoregressive generation of triplets. However, this concatenation method may lead to sequential dependencies among unrelated triplets, introducing error accumulation during decoding. To address this issue, we propose a term-prompted and dual-path text generation (TePDuP) method. This method first utilizes machine reading comprehension (MRC) to extract aspect and opinion term in parallel, and then uses them as prompt prefixes to guide conditional triplet generation, forming a dual-path text generation framework. Meanwhile, during the training phase, we incorporate scheduled sampling as a corrective measure to mitigate the bias stemming from MRC extraction. Furthermore, in order to enhance performance to an even greater extent, we incorporate generation probabilities to merge outcomes guided by aspect and opinion terms, thereby augmenting the resilience of the model. Experimental results on the ASTE-DATA-V2 dataset show that the proposed method is effective and significantly outperforms other baseline models, and provide case studies to demonstrate that the method solves the aforementioned problem to some extent.
- aspect based sentiment analysis,
- triplet extraction,
- text generation,
- machine reading comprehension (MRC),
- scheduled sampling

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References (53)

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