BoschAI @ Causal News Corpus 2023: Robust Cause-Effect Span Extraction using Multi-Layer Sequence Tagging and Data Augmentation
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 16 references that are in the map. First posted on arXiv 11 Dec 2023.
Builds on
9 of 16 references.
References · 9
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
- 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
- 2019Neural Architectures for Nested NER through Linearizationcited by 56
- 2022The Causal News Corpus: Annotating Causal Relations in Event Sentences from Newscited by 24
- 2020Applying Occam's Razor to Transformer-Based Dependency Parsing: What Works, What Doesn't, and What is Really Necessarycited by 8
- 20221Cademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position Selectorcited by 3
- 2022Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 2022cited by 3
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Citing papers per year: 2026: 1.
1 paper.