To Aggregate or Not to Aggregate. That is the Question: A Case Study on Annotation Subjectivity in Span Prediction

Fuente: arXiv
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Autores principales: Kurniawan, Kemal, Mistica, Meladel, Baldwin, Timothy, Lau, Jey Han
Formato: Preprint
Publicado: 2024
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author Kurniawan, Kemal
Mistica, Meladel
Baldwin, Timothy
Lau, Jey Han
author_facet Kurniawan, Kemal
Mistica, Meladel
Baldwin, Timothy
Lau, Jey Han
contents 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, especially in the face of legally-imprecise descriptions of issues. Experiments show that training on majority-voted spans outperforms training on disaggregated ones.
format Preprint
id arxiv_https___arxiv_org_abs_2408_02257
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle To Aggregate or Not to Aggregate. That is the Question: A Case Study on Annotation Subjectivity in Span Prediction
Kurniawan, Kemal
Mistica, Meladel
Baldwin, Timothy
Lau, Jey Han
Computation and Language
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, especially in the face of legally-imprecise descriptions of issues. Experiments show that training on majority-voted spans outperforms training on disaggregated ones.
title To Aggregate or Not to Aggregate. That is the Question: A Case Study on Annotation Subjectivity in Span Prediction
topic Computation and Language
url https://arxiv.org/abs/2408.02257