COM-MRC: A COntext-Masked Machine Reading Comprehension Framework for Aspect Sentiment Triplet Extraction

Zhai, Zepeng; Wen, Hao; Feng, Fan; Li, Ruifan; Wang, Xiaojie

doi:10.18653/v1/2022.emnlp-main.212

Cited by 9 publications

(1 citation statement)

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“…Their expanded versions DECNN (Xu et al, 2018;Chen and Qian, 2020) and BERT-PT (Xu et al, 2019;Wang et al, 2021; are generally adopted as backbones in the subsequent studies. In addition, BERT is utilized as the pedestal for context-aware encoding in a series of more complex tasks, including Aspect-Sentiment Triplet Extraction (ASTE) (Chen et al, 2022a;Zhang et al, 2022b;Chen et al, 2022c;Zhang et al, 2022a;Chen et al, 2022d;Zhao et al, 2022b) and MRC-based ASTE (Yang and Zhao, 2022;Zhai et al, 2022). Recently, the generative framework is introduced into the studies of ASTE, and accordingly BART (Yan et al, 2021;Zhao et al, 2022a) and T5 Hu et al, 2022) are used.…”

Section: Related Workmentioning

confidence: 99%

Smart “Chef”: Verifying the Effect of Role-based Paraphrasing for Aspect Term Extraction

Chen,

Hong,

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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We tackle Aspect Term Extraction (ATE), a task of automatically extracting aspect terms from sentences. The current Pretrained Language Model (PLM) based extractors have achieved significant improvements. They primarily benefit from context-aware encoding. However, a considerable number of sentences in ATE corpora contain uninformative or low-quality contexts. Such sentences frequently act as "troublemakers" during test. In this study, we explore the context-oriented quality improvement method. Specifically, we propose to automatically rewrite the sentences from the perspectives of virtual experts with different roles, such as a "chef" in the restaurant domain. On this basis, we perform ATE over the paraphrased sentences during test, using the well-trained extractors without any change. In the experiments, we leverage ChatGPT to determine virtual experts in the considered domains, and induce ChatGPT to generate paraphrases conditioned on the roles of virtual experts. We experiment on the benchmark SemEval datasets, including Laptop-domain L14 and Restaurantdomain R14-16. The experimental results show that our approach effectively recalls the inconspicuous aspect terms like "al di la", although it reduces the precision. In addition, it is proven that our approach can be substantially improved by redundancy elimination and multi-role voting. More importantly, our approach can be used to expand the predictions obtained on the original sentences. This yields state-of-the-art performance (i.e., F 1-scores of 86.2%, 89.3%, 77.7%, 82.7% on L14 and R14-16) without retraining or fine-tuning the baseline extractors.

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