SemEval-2022的UU-Tax任务3：通过数据增强提高用于分类法分类的语言模型的泛化能力
UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation
摘要：
本文介绍了我们的策略，以解决SemEval-2022任务3 PreTENS：预设分类评估神经网络语义。任务的目标是根据句子中包含的名词对之间的分类关系来确定句子是否被认为是可接受的。对于子任务1 --二进制分类--我们提出了一种有效的方法来增强语言模型的鲁棒性和泛化能力，以便在这个下游任务上进行更好的分类。我们设计了一个两阶段的微调程序的ELECTRA语言模型使用数据增强技术。使用多任务学习和数据丰富的微调进行了严格的实验。实验结果表明，我们提出的模型，UU-Tax，确实能够很好地推广我们的下游任务。对于子任务2 -回归-我们提出了一个简单的分类器，它训练从通用句子编码器（USE）获得的特征。除了描述提交的系统之外，我们还讨论了采用预训练语言模型和数据增强技术的其他实验。对于这两个子任务，我们执行错误分析，以进一步了解所提出的模型的行为。我们在子任务1中实现了91.25%的全局F1_Binary得分，在子任务2中实现了0.221的rho得分。
英文摘要：
This paper presents our strategy to address the SemEval-2022 Task 3 PreTENS: Presupposed Taxonomies Evaluating Neural Network Semantics. The goal of the task is to identify if a sentence is deemed acceptable or not, depending on the taxonomic relationship that holds between a noun pair contained in the sentence. For sub-task 1 -- binary classification -- we propose an effective way to enhance the robustness and the generalizability of language models for better classification on this downstream task. We design a two-stage fine-tuning procedure on the ELECTRA language model using data augmentation techniques. Rigorous experiments are carried out using multi-task learning and data-enriched fine-tuning. Experimental results demonstrate that our proposed model, UU-Tax, is indeed able to generalize well for our downstream task. For sub-task 2 -- regression -- we propose a simple classifier that trains on features obtained from Universal Sentence Encoder (USE). In addition to describing the submitted systems, we discuss other experiments that employ pre-trained language models and data augmentation techniques. For both sub-tasks, we perform error analysis to further understand the behaviour of the proposed models. We achieved a global F1_Binary score of 91.25% in sub-task 1 and a rho score of 0.221 in sub-task 2.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
