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    <abstract>Aspect term extraction aims to extract aspect terms from a review sentence that users have expressed opinions on. One of the remaining challenges for aspect term extraction resides in the lack of sufficient annotated data. While self-training is potentially an effective method to address this issue, the pseudo-labels it yields on unlabeled data could induce noise. In this paper, we use two means to alleviate the noise in the pseudo-labels. One is that inspired by the curriculum learning, we refine the conventional self-training to progressive self-training. Specifically, the base model infers pseudo-labels on a progressive subset at each iteration, where samples in the subset become harder and more numerous as the iteration proceeds. The other is that we use a discriminator to filter the noisy pseudo-labels. Experimental results on four SemEval datasets show that our model significantly outperforms the previous baselines and achieves state-of-the-art performance.</abstract>
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%0 Conference Proceedings
%T Progressive Self-Training with Discriminator for Aspect Term Extraction
%A Wang, Qianlong
%A Wen, Zhiyuan
%A Zhao, Qin
%A Yang, Min
%A Xu, Ruifeng
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F wang-etal-2021-progressive
%X Aspect term extraction aims to extract aspect terms from a review sentence that users have expressed opinions on. One of the remaining challenges for aspect term extraction resides in the lack of sufficient annotated data. While self-training is potentially an effective method to address this issue, the pseudo-labels it yields on unlabeled data could induce noise. In this paper, we use two means to alleviate the noise in the pseudo-labels. One is that inspired by the curriculum learning, we refine the conventional self-training to progressive self-training. Specifically, the base model infers pseudo-labels on a progressive subset at each iteration, where samples in the subset become harder and more numerous as the iteration proceeds. The other is that we use a discriminator to filter the noisy pseudo-labels. Experimental results on four SemEval datasets show that our model significantly outperforms the previous baselines and achieves state-of-the-art performance.
%R 10.18653/v1/2021.emnlp-main.23
%U https://aclanthology.org/2021.emnlp-main.23
%U https://doi.org/10.18653/v1/2021.emnlp-main.23
%P 257-268

Markdown (Informal)

[Progressive Self-Training with Discriminator for Aspect Term Extraction](https://aclanthology.org/2021.emnlp-main.23) (Wang et al., EMNLP 2021)

• Progressive Self-Training with Discriminator for Aspect Term Extraction (Wang et al., EMNLP 2021)

ACL

• Qianlong Wang, Zhiyuan Wen, Qin Zhao, Min Yang, and Ruifeng Xu. 2021. Progressive Self-Training with Discriminator for Aspect Term Extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 257–268, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.