@inproceedings{tandon-chatterjee-2023-lrl,
title = "{LRL}{\_}{NC} at {S}em{E}val-2023 Task 4: The Touche23-{G}eorge-boole Approach for Multi-Label Classification of Human-Values behind Arguments",
author = "Tandon, Kushagri and
Chatterjee, Niladri",
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-1.19",
doi = "10.18653/v1/2023.semeval-1.19",
pages = "136--142",
abstract = "The task ValueEval aims at assigning a sub- set of possible human value categories under- lying a given argument. Values behind argu- ments are often determinants to evaluate the relevance and importance of decisions in eth- ical sense, thereby making them essential for argument mining. The work presented here proposes two systems for the same. Both sys- tems use RoBERTa to encode sentences in each document. System1 makes use of features ob- tained from training models for two auxiliary tasks, whereas System2 combines RoBERTa with topic modeling to get sentence represen- tation. These features are used by a classifi- cation head to generate predictions. System1 secured the rank 22 in the official task rank- ing, achieving the macro F1-score 0.46 on the main dataset. System2 was not a part of official evaluation. Subsequent experiments achieved highest (among the proposed systems) macro F1-scores of 0.48 (System2), 0.31 (ablation on System1) and 0.33 (ablation on System1) on the main dataset, the Nahj al-Balagha dataset, and the New York Times dataset.",
}
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<title>LRL_NC at SemEval-2023 Task 4: The Touche23-George-boole Approach for Multi-Label Classification of Human-Values behind Arguments</title>
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<abstract>The task ValueEval aims at assigning a sub- set of possible human value categories under- lying a given argument. Values behind argu- ments are often determinants to evaluate the relevance and importance of decisions in eth- ical sense, thereby making them essential for argument mining. The work presented here proposes two systems for the same. Both sys- tems use RoBERTa to encode sentences in each document. System1 makes use of features ob- tained from training models for two auxiliary tasks, whereas System2 combines RoBERTa with topic modeling to get sentence represen- tation. These features are used by a classifi- cation head to generate predictions. System1 secured the rank 22 in the official task rank- ing, achieving the macro F1-score 0.46 on the main dataset. System2 was not a part of official evaluation. Subsequent experiments achieved highest (among the proposed systems) macro F1-scores of 0.48 (System2), 0.31 (ablation on System1) and 0.33 (ablation on System1) on the main dataset, the Nahj al-Balagha dataset, and the New York Times dataset.</abstract>
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%0 Conference Proceedings
%T LRL_NC at SemEval-2023 Task 4: The Touche23-George-boole Approach for Multi-Label Classification of Human-Values behind Arguments
%A Tandon, Kushagri
%A Chatterjee, Niladri
%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 tandon-chatterjee-2023-lrl
%X The task ValueEval aims at assigning a sub- set of possible human value categories under- lying a given argument. Values behind argu- ments are often determinants to evaluate the relevance and importance of decisions in eth- ical sense, thereby making them essential for argument mining. The work presented here proposes two systems for the same. Both sys- tems use RoBERTa to encode sentences in each document. System1 makes use of features ob- tained from training models for two auxiliary tasks, whereas System2 combines RoBERTa with topic modeling to get sentence represen- tation. These features are used by a classifi- cation head to generate predictions. System1 secured the rank 22 in the official task rank- ing, achieving the macro F1-score 0.46 on the main dataset. System2 was not a part of official evaluation. Subsequent experiments achieved highest (among the proposed systems) macro F1-scores of 0.48 (System2), 0.31 (ablation on System1) and 0.33 (ablation on System1) on the main dataset, the Nahj al-Balagha dataset, and the New York Times dataset.
%R 10.18653/v1/2023.semeval-1.19
%U https://aclanthology.org/2023.semeval-1.19
%U https://doi.org/10.18653/v1/2023.semeval-1.19
%P 136-142
Markdown (Informal)
[LRL_NC at SemEval-2023 Task 4: The Touche23-George-boole Approach for Multi-Label Classification of Human-Values behind Arguments](https://aclanthology.org/2023.semeval-1.19) (Tandon & Chatterjee, SemEval 2023)
ACL