HeadlineCause: A Dataset of News Headlines for Detecting Causalities
This provides a resource for researchers in natural language processing to improve causal reasoning in news analysis, though it is incremental as it builds on existing datasets.
The authors tackled the problem of detecting implicit causal relations in news headlines by creating HeadlineCause, a dataset with over 14,000 labeled headline pairs in English and Russian, and demonstrated its validity with models like XLM-RoBERTa and GPT-2.
Detecting implicit causal relations in texts is a task that requires both common sense and world knowledge. Existing datasets are focused either on commonsense causal reasoning or explicit causal relations. In this work, we present HeadlineCause, a dataset for detecting implicit causal relations between pairs of news headlines. The dataset includes over 5000 headline pairs from English news and over 9000 headline pairs from Russian news labeled through crowdsourcing. The pairs vary from totally unrelated or belonging to the same general topic to the ones including causation and refutation relations. We also present a set of models and experiments that demonstrates the dataset validity, including a multilingual XLM-RoBERTa based model for causality detection and a GPT-2 based model for possible effects prediction.