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Financial Document Causality Detection Shared Task (FinCausal 2020)

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arxiv 2012.02505 v1 pith:T2NEOEWP submitted 2020-12-04 cs.CL stat.ML

Dominique Mariko , Hanna Abi Akl , Estelle Labidurie , Stéphane Durfort , Hugues de Mazancourt , Mahmoud El-Haj This is my paper · ORCID
classification cs.CLstat.ML
keywords taskfinancialfincausalassociatedcausalitydetectionsharedworkshop
verification ladder T0 review T1 audit T2 compute T3 formal
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We present the FinCausal 2020 Shared Task on Causality Detection in Financial Documents and the associated FinCausal dataset, and discuss the participating systems and results. Two sub-tasks are proposed: a binary classification task (Task 1) and a relation extraction task (Task 2). A total of 16 teams submitted runs across the two Tasks and 13 of them contributed with a system description paper. This workshop is associated to the Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020), held at The 28th International Conference on Computational Linguistics (COLING'2020), Barcelona, Spain on September 12, 2020.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability Report Generation

    cs.CL 2024-12 reject novelty 5.0 of 10

    Small fine-tuned models on SusGen-30K are reported to nearly match GPT-4 on financial and ESG tasks, with a new TCFD-Bench benchmark, though the comparison is biased.

  2. Open FinLLM Leaderboard: Towards Financial AI Readiness

    cs.CE 2025-01 conditional novelty 3.0 of 10

    The paper presents an open, continuously updated FinLLM leaderboard that aggregates existing financial benchmarks and demos for comparing models.

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