Progressive Self-Training with Discriminator for Aspect Term Extraction
Qianlong Wang, Zhiyuan Wen, Qin Zhao, Min Yang, Ruifeng Xu · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
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-ofthe-art performance.