Abstract
The purpose of Aspect Sentiment Triplet Extraction (ASTE) is to extract a triplet, including the target or aspect, its associated sentiment, and related opinion terms that explain the underlying cause of the sentiment. Some recent studies fail to capture the strong interdependence between ATE and OTE, while others fail to effectively introduce the relationship between aspects and opinions into sentiment classification tasks. To solve these problems, we construct a multi-round machine reading comprehension framework based on a rethink mechanism to solve ASTE tasks efficiently. The rethink mechanism allows the framework to model complex relationships between entities, and exclusive classifiers and probability generation algorithms can reduce query conflicts and unilateral drops in probability. Besides, the multi-round structure can fuse explicit semantic information flow between aspect, opinion and sentiment. Extensive experiments show that the proposed model achieves the most advanced effect and can be effectively applied to ASTE tasks.
Supported by the Social and Science Foundation of Liaoning Province (No. L20BTQ008).
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Acknowledgements
This work is supported by a grant from the Social and Science Foundation of Liaoning Province (No. L20BTQ008).
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Zhang, F., Zhang, Y., Wang, M., Yang, H., Lu, M., Yang, L. (2023). Self Question-Answering: Aspect Sentiment Triplet Extraction via a Multi-MRC Framework Based on Rethink Mechanism. In: Sun, M., et al. Chinese Computational Linguistics. CCL 2023. Lecture Notes in Computer Science(), vol 14232. Springer, Singapore. https://doi.org/10.1007/978-981-99-6207-5_14
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