@inproceedings{saha-etal-2022-spock-causal,
title = "{SPOCK} @ Causal News Corpus 2022: Cause-Effect-Signal Span Detection Using Span-Based and Sequence Tagging Models",
author = "Saha, Anik and
Gittens, Alex and
Ni, Jian and
Hassanzadeh, Oktie and
Yener, Bulent and
Srinivas, Kavitha",
editor = {H{\"u}rriyeto{\u{g}}lu, Ali and
Tanev, Hristo and
Zavarella, Vanni and
Y{\"o}r{\"u}k, Erdem},
booktitle = "Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.case-1.18",
doi = "10.18653/v1/2022.case-1.18",
pages = "133--137",
abstract = "Understanding causal relationship is an importance part of natural language processing. We address the causal information extraction problem with different neural models built on top of pre-trained transformer-based language models for identifying Cause, Effect and Signal spans, from news data sets. We use the Causal News Corpus subtask 2 training data set to train span-based and sequence tagging models. Our span-based model based on pre-trained BERT base weights achieves an F1 score of 47.48 on the test set with an accuracy score of 36.87 and obtained 3rd place in the Causal News Corpus 2022 shared task.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="saha-etal-2022-spock-causal">
<titleInfo>
<title>SPOCK @ Causal News Corpus 2022: Cause-Effect-Signal Span Detection Using Span-Based and Sequence Tagging Models</title>
</titleInfo>
<name type="personal">
<namePart type="given">Anik</namePart>
<namePart type="family">Saha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alex</namePart>
<namePart type="family">Gittens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jian</namePart>
<namePart type="family">Ni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Oktie</namePart>
<namePart type="family">Hassanzadeh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bulent</namePart>
<namePart type="family">Yener</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kavitha</namePart>
<namePart type="family">Srinivas</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ali</namePart>
<namePart type="family">Hürriyetoğlu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hristo</namePart>
<namePart type="family">Tanev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vanni</namePart>
<namePart type="family">Zavarella</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Erdem</namePart>
<namePart type="family">Yörük</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Abu Dhabi, United Arab Emirates (Hybrid)</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Understanding causal relationship is an importance part of natural language processing. We address the causal information extraction problem with different neural models built on top of pre-trained transformer-based language models for identifying Cause, Effect and Signal spans, from news data sets. We use the Causal News Corpus subtask 2 training data set to train span-based and sequence tagging models. Our span-based model based on pre-trained BERT base weights achieves an F1 score of 47.48 on the test set with an accuracy score of 36.87 and obtained 3rd place in the Causal News Corpus 2022 shared task.</abstract>
<identifier type="citekey">saha-etal-2022-spock-causal</identifier>
<identifier type="doi">10.18653/v1/2022.case-1.18</identifier>
<location>
<url>https://aclanthology.org/2022.case-1.18</url>
</location>
<part>
<date>2022-12</date>
<extent unit="page">
<start>133</start>
<end>137</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T SPOCK @ Causal News Corpus 2022: Cause-Effect-Signal Span Detection Using Span-Based and Sequence Tagging Models
%A Saha, Anik
%A Gittens, Alex
%A Ni, Jian
%A Hassanzadeh, Oktie
%A Yener, Bulent
%A Srinivas, Kavitha
%Y Hürriyetoğlu, Ali
%Y Tanev, Hristo
%Y Zavarella, Vanni
%Y Yörük, Erdem
%S Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F saha-etal-2022-spock-causal
%X Understanding causal relationship is an importance part of natural language processing. We address the causal information extraction problem with different neural models built on top of pre-trained transformer-based language models for identifying Cause, Effect and Signal spans, from news data sets. We use the Causal News Corpus subtask 2 training data set to train span-based and sequence tagging models. Our span-based model based on pre-trained BERT base weights achieves an F1 score of 47.48 on the test set with an accuracy score of 36.87 and obtained 3rd place in the Causal News Corpus 2022 shared task.
%R 10.18653/v1/2022.case-1.18
%U https://aclanthology.org/2022.case-1.18
%U https://doi.org/10.18653/v1/2022.case-1.18
%P 133-137
Markdown (Informal)
[SPOCK @ Causal News Corpus 2022: Cause-Effect-Signal Span Detection Using Span-Based and Sequence Tagging Models](https://aclanthology.org/2022.case-1.18) (Saha et al., CASE 2022)
ACL