@inproceedings{saha-etal-2022-spock,
title = "{SPOCK} at {F}in{C}ausal 2022: Causal Information Extraction Using Span-Based and Sequence Tagging Models",
author = "Saha, Anik and
Ni, Jian and
Hassanzadeh, Oktie and
Gittens, Alex and
Srinivas, Kavitha and
Yener, Bulent",
editor = "El-Haj, Mahmoud and
Rayson, Paul and
Zmandar, Nadhem",
booktitle = "Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.fnp-1.17",
pages = "108--111",
abstract = "Causal information extraction is an important task in natural language processing, particularly in finance domain. In this work, we develop several information extraction models using pre-trained transformer-based language models for identifying cause and effect text spans from financial documents. We use FinCausal 2021 and 2022 data sets to train span-based and sequence tagging models. Our ensemble of sequence tagging models based on the RoBERTa-Large pre-trained language model achieves an F1 score of 94.70 with Exact Match score of 85.85 and obtains the 1st place in the FinCausal 2022 competition.",
}
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<abstract>Causal information extraction is an important task in natural language processing, particularly in finance domain. In this work, we develop several information extraction models using pre-trained transformer-based language models for identifying cause and effect text spans from financial documents. We use FinCausal 2021 and 2022 data sets to train span-based and sequence tagging models. Our ensemble of sequence tagging models based on the RoBERTa-Large pre-trained language model achieves an F1 score of 94.70 with Exact Match score of 85.85 and obtains the 1st place in the FinCausal 2022 competition.</abstract>
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%0 Conference Proceedings
%T SPOCK at FinCausal 2022: Causal Information Extraction Using Span-Based and Sequence Tagging Models
%A Saha, Anik
%A Ni, Jian
%A Hassanzadeh, Oktie
%A Gittens, Alex
%A Srinivas, Kavitha
%A Yener, Bulent
%Y El-Haj, Mahmoud
%Y Rayson, Paul
%Y Zmandar, Nadhem
%S Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F saha-etal-2022-spock
%X Causal information extraction is an important task in natural language processing, particularly in finance domain. In this work, we develop several information extraction models using pre-trained transformer-based language models for identifying cause and effect text spans from financial documents. We use FinCausal 2021 and 2022 data sets to train span-based and sequence tagging models. Our ensemble of sequence tagging models based on the RoBERTa-Large pre-trained language model achieves an F1 score of 94.70 with Exact Match score of 85.85 and obtains the 1st place in the FinCausal 2022 competition.
%U https://aclanthology.org/2022.fnp-1.17
%P 108-111
Markdown (Informal)
[SPOCK at FinCausal 2022: Causal Information Extraction Using Span-Based and Sequence Tagging Models](https://aclanthology.org/2022.fnp-1.17) (Saha et al., FNP 2022)
ACL