Understanding causality is a core aspect of intelligence. The Event Causality Identification with Causal News Corpus Shared Task addresses two aspects of this challenge: Subtask 1 aims at detecting causal relationships in texts, and Subtask 2 requires identifying signal words and the spans that refer to the cause or effect, respectively. Our system, which is based on pre-trained transformers, stacked sequence tagging, and synthetic data augmentation, ranks third in Subtask 1 and wins Subtask 2 with an F1 score of 72.8, corresponding to a margin of 13 pp. to the second-best system.
@article{arxiv.2312.06338,
title = {BoschAI @ Causal News Corpus 2023: Robust Cause-Effect Span Extraction using Multi-Layer Sequence Tagging and Data Augmentation},
author = {Timo Pierre Schrader and Simon Razniewski and Lukas Lange and Annemarie Friedrich},
journal= {arXiv preprint arXiv:2312.06338},
year = {2023}
}
Comments
6 pages, 6 tables, 1 figure, published in "Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text"