TartuNLP at EvaLatin 2024: Emotion Polarity Detection

Aleksei Dorkin, Kairit Sirts
5/2/2024
cs.CL

No confident code implementation found for this paper.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Cite this paper

@article{dorkin2024tartunlp,
  title  = {TartuNLP at EvaLatin 2024: Emotion Polarity Detection},
  author = {Aleksei Dorkin and Kairit Sirts},
  year   = {2024},
  eprint = {2405.01159},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.01159v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2405.01159.svg)](https://codeofpaper.com/paper/2405.01159)

Discussion