TartuNLP at EvaLatin 2024: Emotion Polarity Detection

Fuente: arXiv
Guardado en:
Detalles Bibliográficos
Autores principales: Dorkin, Aleksei, Sirts, Kairit
Formato: Preprint
Publicado: 2024
Materias:
Acceso en línea:
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
_version_ 1866916513615708160
author Dorkin, Aleksei
Sirts, Kairit
author_facet Dorkin, Aleksei
Sirts, Kairit
contents This paper presents the TartuNLP team submission to EvaLatin 2024 shared task of the emotion polarity detection for historical Latin texts. Our system relies on two distinct approaches to annotating training data for supervised learning: 1) creating heuristics-based labels by adopting the polarity lexicon provided by the organizers and 2) generating labels with GPT4. We employed parameter efficient fine-tuning using the adapters framework and experimented with both monolingual and cross-lingual knowledge transfer for training language and task adapters. Our submission with the LLM-generated labels achieved the overall first place in the emotion polarity detection task. Our results show that LLM-based annotations show promising results on texts in Latin.
format Preprint
id arxiv_https___arxiv_org_abs_2405_01159
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle TartuNLP at EvaLatin 2024: Emotion Polarity Detection
Dorkin, Aleksei
Sirts, Kairit
Computation and Language
This paper presents the TartuNLP team submission to EvaLatin 2024 shared task of the emotion polarity detection for historical Latin texts. Our system relies on two distinct approaches to annotating training data for supervised learning: 1) creating heuristics-based labels by adopting the polarity lexicon provided by the organizers and 2) generating labels with GPT4. We employed parameter efficient fine-tuning using the adapters framework and experimented with both monolingual and cross-lingual knowledge transfer for training language and task adapters. Our submission with the LLM-generated labels achieved the overall first place in the emotion polarity detection task. Our results show that LLM-based annotations show promising results on texts in Latin.
title TartuNLP at EvaLatin 2024: Emotion Polarity Detection
topic Computation and Language
url https://arxiv.org/abs/2405.01159