#Upb of first and second notes code#
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#Upb of first and second notes mods#
Cite (Informal): UPB at FinCausal-2020, Tasks 1 & 2: Causality Analysis in Financial Documents using Pretrained Language Models (Ionescu et al., FNP 2020) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: Code = ".
In Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pages 55–59, Barcelona, Spain (Online). UPB at FinCausal-2020, Tasks 1 & 2: Causality Analysis in Financial Documents using Pretrained Language Models. Anthology ID: 2020.fnp-1.8 Volume: Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation Month: December Year: 2020 Address: Barcelona, Spain (Online) Venue: FNP SIG: Publisher: COLING Note: Pages: 55–59 Language: URL: DOI: Bibkey: ionescu-etal-2020-upb Cite (ACL): Marius Ionescu, Andrei-Marius Avram, George-Andrei Dima, Dumitru-Clementin Cercel, and Mihai Dascalu. Subsequently, a BERT model was fine-tuned for the second task and a Conditional Random Field model was used on top of the generated language features the system managed to identify the cause and effect relationships with an F1-score of 73.10%. Various Transformer-based language models were fine-tuned for the first task and we obtained the second place in the competition with an F1-score of 97.55% using an ensemble of five such language models. The competition is divided into two tasks: (a) a binary classification task for determining whether sentences are causal or not, and (b) a sequence labeling task aimed at identifying elements related to cause and effect. FinCausal 2020 - Causality Identification in Financial Documents – is a competition targeting to boost results in financial causality by obtaining an explanation of how different individual events or chain of events interact and generate subsequent events in a financial environment. Abstract Financial causality detection is centered on identifying connections between different assets from financial news in order to improve trading strategies.
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