  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.
