IDIAPers @ Causal News Corpus 2022：通过基于提示的少样本方法实现高效因果关系识别
IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach
AI总结：
本研究针对因果关系识别任务，提出将其转化为掩码语言建模问题的基于提示的少样本微调方法，仅用15.7%的训练数据就取得了接近全量数据集成模型的优异性能。
摘要：
本文介绍了我们参与CASE-2022子任务1——使用Causal News Corpus（因果新闻语料库）进行事件因果关系识别——的相关工作。我们针对因果关系识别（CRI）任务，采用了一组简单且互补的技术，在少量标注样本（即少样本设置）下微调语言模型（LMs）。我们遵循基于提示的预测方法来微调语言模型，将CRI任务转化为掩码语言建模（MLM）问题。该方法使得原生以MLM任务预训练的语言模型能够直接生成针对CRI特定提示的文本响应。我们将该方法的性能与在全量数据集上训练的集成技术进行了对比。我们表现最佳的提交模型仅使用每类256个样本（占全部可用数据的15.7%）进行微调，却取得了第二高的精确率（0.82）、第三高的准确率（0.82），且F1值（0.85）非常接近冠军团队报告的结果（0.86）。
英文摘要：
In this paper, we describe our participation in the subtask 1 of CASE-2022, Event Causality Identification with Casual News Corpus. We address the Causal Relation Identification (CRI) task by exploiting a set of simple yet complementary techniques for fine-tuning language models (LMs) on a small number of annotated examples (i.e., a few-shot configuration). We follow a prompt-based prediction approach for fine-tuning LMs in which the CRI task is treated as a masked language modeling problem (MLM). This approach allows LMs natively pre-trained on MLM problems to directly generate textual responses to CRI-specific prompts. We compare the performance of this method against ensemble techniques trained on the entire dataset. Our best-performing submission was fine-tuned with only 256 instances per class, 15.7% of the all available data, and yet obtained the second-best precision (0.82), third-best accuracy (0.82), and an F1-score (0.85) very close to what was reported by the winner team (0.86).
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。
