@inproceedings{dhrangadhariya-etal-2023-hevs,
title = "{HEVS}-{TUW} at {S}em{E}val-2023 Task 8: Ensemble of Language Models and Rule-based Classifiers for Claims Identification and {PICO} Extraction",
author = {Dhrangadhariya, Anjani and
Kusa, Wojciech and
M{\"u}ller, Henning and
Hanbury, Allan},
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.246",
doi = "10.18653/v1/2023.semeval-1.246",
pages = "1776--1782",
abstract = "This paper describes the HEVS-TUW team submission to the SemEval-2023 Task 8: Causal Claims. We participated in two subtasks: (1) causal claims detection and (2) PIO identification. For subtask 1, we experimented with an ensemble of weakly supervised question detection and fine-tuned Transformer-based models. For subtask 2 of PIO frame extraction, we used a combination of deep representation learning and a rule-based approach. Our best model for subtask 1 ranks fourth with an F1-score of 65.77{\%}. It shows moderate benefit from ensembling models pre-trained on independent categories. The results for subtask 2 warrant further investigation for improvement.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="dhrangadhariya-etal-2023-hevs">
<titleInfo>
<title>HEVS-TUW at SemEval-2023 Task 8: Ensemble of Language Models and Rule-based Classifiers for Claims Identification and PICO Extraction</title>
</titleInfo>
<name type="personal">
<namePart type="given">Anjani</namePart>
<namePart type="family">Dhrangadhariya</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wojciech</namePart>
<namePart type="family">Kusa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Henning</namePart>
<namePart type="family">Müller</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Allan</namePart>
<namePart type="family">Hanbury</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Atul</namePart>
<namePart type="given">Kr.</namePart>
<namePart type="family">Ojha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">A</namePart>
<namePart type="given">Seza</namePart>
<namePart type="family">Doğruöz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Giovanni</namePart>
<namePart type="family">Da San Martino</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Harish</namePart>
<namePart type="family">Tayyar Madabushi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ritesh</namePart>
<namePart type="family">Kumar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elisa</namePart>
<namePart type="family">Sartori</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Toronto, Canada</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper describes the HEVS-TUW team submission to the SemEval-2023 Task 8: Causal Claims. We participated in two subtasks: (1) causal claims detection and (2) PIO identification. For subtask 1, we experimented with an ensemble of weakly supervised question detection and fine-tuned Transformer-based models. For subtask 2 of PIO frame extraction, we used a combination of deep representation learning and a rule-based approach. Our best model for subtask 1 ranks fourth with an F1-score of 65.77%. It shows moderate benefit from ensembling models pre-trained on independent categories. The results for subtask 2 warrant further investigation for improvement.</abstract>
<identifier type="citekey">dhrangadhariya-etal-2023-hevs</identifier>
<identifier type="doi">10.18653/v1/2023.semeval-1.246</identifier>
<location>
<url>https://aclanthology.org/2023.semeval-1.246</url>
</location>
<part>
<date>2023-07</date>
<extent unit="page">
<start>1776</start>
<end>1782</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T HEVS-TUW at SemEval-2023 Task 8: Ensemble of Language Models and Rule-based Classifiers for Claims Identification and PICO Extraction
%A Dhrangadhariya, Anjani
%A Kusa, Wojciech
%A Müller, Henning
%A Hanbury, Allan
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F dhrangadhariya-etal-2023-hevs
%X This paper describes the HEVS-TUW team submission to the SemEval-2023 Task 8: Causal Claims. We participated in two subtasks: (1) causal claims detection and (2) PIO identification. For subtask 1, we experimented with an ensemble of weakly supervised question detection and fine-tuned Transformer-based models. For subtask 2 of PIO frame extraction, we used a combination of deep representation learning and a rule-based approach. Our best model for subtask 1 ranks fourth with an F1-score of 65.77%. It shows moderate benefit from ensembling models pre-trained on independent categories. The results for subtask 2 warrant further investigation for improvement.
%R 10.18653/v1/2023.semeval-1.246
%U https://aclanthology.org/2023.semeval-1.246
%U https://doi.org/10.18653/v1/2023.semeval-1.246
%P 1776-1782
Markdown (Informal)
[HEVS-TUW at SemEval-2023 Task 8: Ensemble of Language Models and Rule-based Classifiers for Claims Identification and PICO Extraction](https://aclanthology.org/2023.semeval-1.246) (Dhrangadhariya et al., SemEval 2023)
ACL