@inproceedings{kurniawan-etal-2024-aggregate,
title = "To Aggregate or Not to Aggregate. That is the Question: A Case Study on Annotation Subjectivity in Span Prediction",
author = "Kurniawan, Kemal and
Mistica, Meladel and
Baldwin, Timothy and
Lau, Jey Han",
editor = "De Clercq, Orph{\'e}e and
Barriere, Valentin and
Barnes, Jeremy and
Klinger, Roman and
Sedoc, Jo{\~a}o and
Tafreshi, Shabnam",
booktitle = "Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.wassa-1.29",
pages = "362--368",
abstract = "This paper explores the task of automatic prediction of text spans in a legal problem description that support a legal area label. We use a corpus of problem descriptions written by laypeople in English that is annotated by practising lawyers. Inherent subjectivity exists in our task because legal area categorisation is a complex task, and lawyers often have different views on a problem. Experiments show that training on majority-voted spans outperforms training on disaggregated ones.",
}
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<abstract>This paper explores the task of automatic prediction of text spans in a legal problem description that support a legal area label. We use a corpus of problem descriptions written by laypeople in English that is annotated by practising lawyers. Inherent subjectivity exists in our task because legal area categorisation is a complex task, and lawyers often have different views on a problem. Experiments show that training on majority-voted spans outperforms training on disaggregated ones.</abstract>
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%0 Conference Proceedings
%T To Aggregate or Not to Aggregate. That is the Question: A Case Study on Annotation Subjectivity in Span Prediction
%A Kurniawan, Kemal
%A Mistica, Meladel
%A Baldwin, Timothy
%A Lau, Jey Han
%Y De Clercq, Orphée
%Y Barriere, Valentin
%Y Barnes, Jeremy
%Y Klinger, Roman
%Y Sedoc, João
%Y Tafreshi, Shabnam
%S Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F kurniawan-etal-2024-aggregate
%X This paper explores the task of automatic prediction of text spans in a legal problem description that support a legal area label. We use a corpus of problem descriptions written by laypeople in English that is annotated by practising lawyers. Inherent subjectivity exists in our task because legal area categorisation is a complex task, and lawyers often have different views on a problem. Experiments show that training on majority-voted spans outperforms training on disaggregated ones.
%U https://aclanthology.org/2024.wassa-1.29
%P 362-368
Markdown (Informal)
[To Aggregate or Not to Aggregate. That is the Question: A Case Study on Annotation Subjectivity in Span Prediction](https://aclanthology.org/2024.wassa-1.29) (Kurniawan et al., WASSA-WS 2024)
ACL