System behavior is often based on causal relations between certain events (e.g. If event1, then event2). Consequently, those causal relations are also textually embedded in requirements. We want to extract this causal knowledge and utilize it to derive test cases automatically and to reason about dependencies between requirements. Existing NLP approaches fail to extract causality from natural language (NL) with reasonable performance. In this paper, we describe first steps towards building a new approach for causality extraction and contribute: (1) an NLP architecture based on Tree Recursive Neural Networks (TRNN) that we will train to identify causal relations in NL requirements and (2) an annotation scheme and a dataset that is suitable for training TRNNs. Our dataset contains 212,186 sentences from 463 publicly available requirement documents and is a first step towards a gold standard corpus for causality extraction. We encourage fellow researchers to contribute to our dataset and help us in finalizing the causality annotation process. Additionally, the dataset can also be annotated further to serve as a benchmark for other RE-relevant NLP tasks such as requirements classification.

中文翻译：

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从需求中提取因果关系

系统行为通常基于某些事件之间的因果关系（例如，如果事件 1，则事件 2）。因此，这些因果关系也以文字形式嵌入到需求中。我们想提取这种因果知识并利用它来自动推导出测试用例并推理需求之间的依赖关系。现有的 NLP 方法无法以合理的性能从自然语言 (NL) 中提取因果关系。在本文中，我们描述了构建因果关系提取新方法的第一步，并做出了贡献：(1) 基于树递归神经网络 (TRNN) 的 NLP 架构，我们将对其进行训练以识别 NL 要求中的因果关系，以及 (2)注释方案和适合训练 TRNN 的数据集。我们的数据集包含 212，来自 463 个公开可用的需求文档中的 186 个句子，这是迈向因果关系提取黄金标准语料库的第一步。我们鼓励其他研究人员为我们的数据集做出贡献，并帮助我们完成因果关系注释过程。此外，数据集还可以进一步注释，作为其他与 RE 相关的 NLP 任务（例如需求分类）的基准。