1Cademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position Selector
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Abstract (arXiv)
In this paper, we present our approach and empirical observations for Cause-Effect Signal Span Detection -- Subtask 2 of Shared task 3~citetan-etal-2022-event at CASE 2022. The shared task aims to extract the cause, effect, and signal spans from a given causal sentence. We model the task as a reading comprehension (RC) problem and apply a token-level RC-based span prediction paradigm to the task as the baseline. We explore different training objectives to fine-tune the model, as well as data augmentation (DA) tricks based on the language model (LM) for performance improvement. Additionally, we propose an efficient beam-search post-processing strategy to due with the drawbacks of span detection to obtain a further performance gain. Our approach achieves an average F1 score of 54.15 and ranks textbf1st in the CASE competition. Our code is available at urlhttps://github.com/Gzhang-umich/1CademyTeamOfCASE.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 12 of its 16 references that are in the map. First posted on arXiv 31 Oct 2022.
Builds on
12 of 16 references.
References · 12
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Citing papers per year: 2022: 1; 2023: 2.
3 papers.
- 2022Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 2022cited by 3
- 2023BoschAI @ Causal News Corpus 2023: Robust Cause-Effect Span Extraction using Multi-Layer Sequence Tagging and Data Augmentationcited by 1
- 2023Constructing and Interpreting Causal Knowledge Graphs from Newscited by 0
- 2023BoschAI @ Causal News Corpus 2023: Robust Cause-Effect Span Extraction using Multi-Layer Sequence Tagging and Data Augmentation11 Dec 2023
- 2023Constructing and Interpreting Causal Knowledge Graphs from News16 May 2023
- 2022Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 202222 Nov 2022