Yseop在SemEval-2020任务5：用于反事实陈述分析的级联BERT语言模型
Yseop at SemEval-2020 Task 5: Cascaded BERT Language Model for Counterfactual Statement Analysis
摘要：
在本文中，我们探讨的策略，以检测和评估反事实的句子。我们描述了我们的系统SemEval-2020任务5：建模语言中的因果推理：检测反事实。我们使用一个BERT基本模型的分类任务，并建立一个混合BERT多层感知器系统来处理序列识别任务。我们的实验表明，虽然在分类任务中引入句法和语义特征对改进系统的作用不大，但使用这些类型的特征作为级联线性输入来微调模型的序列界定能力，确保它在第二个任务中优于其他类似目的的复杂系统，如BiLSTM-CRF。我们的系统在任务1和任务2中分别获得了85.00%和83.90%的F1分数。
英文摘要：
In this paper, we explore strategies to detect and evaluate counterfactual sentences. We describe our system for SemEval-2020 Task 5: Modeling Causal Reasoning in Language: Detecting Counterfactuals. We use a BERT base model for the classification task and build a hybrid BERT Multi-Layer Perceptron system to handle the sequence identification task. Our experiments show that while introducing syntactic and semantic features does little in improving the system in the classification task, using these types of features as cascaded linear inputs to fine-tune the sequence-delimiting ability of the model ensures it outperforms other similar-purpose complex systems like BiLSTM-CRF in the second task. Our system achieves an F1 score of 85.00% in Task 1 and 83.90% in 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。
