BoschAI @ Causal News Corpus 2023: Robust Cause-Effect Span Extraction using Multi-Layer Sequence Tagging and Data Augmentation

• URL: http://arxiv.org/abs/2312.06338v1
• Date: Mon, 11 Dec 2023 12:35:35 GMT
• Title: BoschAI @ Causal News Corpus 2023: Robust Cause-Effect Span Extraction using Multi-Layer Sequence Tagging and Data Augmentation
• Authors: Timo Pierre Schrader, Simon Razniewski, Lukas Lange, Annemarie Friedrich
• Abstract summary: 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. 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.
• Score: 16.59785586761074
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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