Summary of Nostra Domina at Evalatin 2024: Improving Latin Polarity Detection Through Data Augmentation, by Stephen Bothwell et al.
Nostra Domina at EvaLatin 2024: Improving Latin Polarity Detection through Data Augmentation
by Stephen Bothwell, Abigail Swenor, David Chiang
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The team Nostra Domina submitted their work to the EvaLatin 2024 shared task, focusing on emotion polarity detection in Latin texts. The task is challenging due to the limited available data and the complexity of sentiment analysis in poetic genres. To overcome these limitations, the authors developed two methods for automatic polarity annotation based on the k-means algorithm. They then employed various Latin large language models (LLMs) within a neural architecture to capture contextual sentiment representations. The best approach achieved a macro-averaged Macro-F_1 score of 0.92 on the shared task’s test set, ranking second overall. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a team that tried to figure out how to detect emotions in old Latin poems and texts. They used special algorithms and computer models to help them do this, because there isn’t much data available for this kind of task. They came up with two ways to do this automatically, using something called the k-means algorithm. Then they used big computers to analyze all this information and get a better understanding of how emotions are expressed in these texts. Their best method was really good, it did almost as well as the best one out there! |
