Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction

• URL: http://arxiv.org/abs/2302.14719v1
• Date: Tue, 28 Feb 2023 16:31:17 GMT
• Title: Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction
• Authors: Kai Sun, Richong Zhang, Samuel Mensah, Nikolaos Aletras, Yongyi Mao, Xudong Liu
• Abstract summary: Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining. Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios. We propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagrees on the unlabelled target data.
• Score: 62.41511766918932
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining that aims to extract the targets (or aspects) on which opinions have been expressed. Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios, where the testing and training distributions differ. Most methods use domain adversarial neural networks that aim to reduce the domain gap between the labelled source and unlabelled target domains to improve target domain performance. However, this approach only aligns feature distributions and does not account for class-wise feature alignment, leading to suboptimal results. Semi-supervised learning (SSL) has been explored as a solution, but is limited by the quality of pseudo-labels generated by the model. Inspired by the theoretical foundations in domain adaptation [2], we propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagree on the unlabelled target data, in an effort to boost the target domain performance. Extensive experiments on benchmark cross-domain OTE datasets show that this approach is effective and performs consistently well in settings with large domain shifts.

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