Shivashankar Subramanian, Ioana Baldini, et al.
IAAI 2020
In this paper, we address the problem of extracting causal knowledge from text documents in a weakly supervised manner. We target use cases in decision support and risk management, where causes and effects are general phrases without any constraints. We present a method called CaKnowLI which only takes as input the text corpus and extracts a high-quality collection of cause-effect pairs in an automated way. We approach this problem using state-of-the-art natural language understanding techniques based on pre-trained neural models for Natural Language Inference (NLI). Finally, we evaluate the proposed method on existing and new benchmark data sets.
Shivashankar Subramanian, Ioana Baldini, et al.
IAAI 2020
Gabriele Picco, Lam Thanh Hoang, et al.
EMNLP 2021
Kevin Gu, Eva Tuecke, et al.
ICML 2024
Hui Wan, Song Feng, et al.
NAACL 2021