Predicting Directionality in Causal Relations in Text
This work addresses causal relation prediction in NLP, but it is incremental as it tests existing models on a specific task.
The study evaluated BERT and SpanBERT for predicting causal direction in text, finding SpanBERT better for longer spans and inter-sentence/implicit relations more challenging.
In this work, we test the performance of two bidirectional transformer-based language models, BERT and SpanBERT, on predicting directionality in causal pairs in the textual content. Our preliminary results show that predicting direction for inter-sentence and implicit causal relations is more challenging. And, SpanBERT performs better than BERT on causal samples with longer span length. We also introduce CREST which is a framework for unifying a collection of scattered datasets of causal relations.