This paper describes submissions from the team Nostra Domina to the EvaLatin
2024 shared task of emotion polarity detection. Given the low-resource
environment of Latin and the complexity of sentiment in rhetorical genres like
poetry, we augmented the available data through automatic polarity annotation.
We present two methods for doing so on the basis of the $k$-means algorithm,
and we employ a variety of Latin large language models (LLMs) in a neural
architecture to better capture the underlying contextual sentiment
representations. Our best approach achieved the second highest macro-averaged
Macro-$F_1$ score on the shared task's test set.

本文描述了 Nostra Domina 团队对 EvaLatin 2024 情感极性检测共享任务的提交。我们通过自动极性注释方法增强了拉丁语的低资源环境以及修辞体裁如诗歌中的情感复杂性，并采用多种拉丁大语言模型（LLMs）的神经架构来更好地捕捉底层上下文情感表示。我们的最佳方法在共享任务的测试集上达到了第二高的宏平均 Macro-F1 分数。

通过数据增强提升拉丁语极性检测的 Nostra Domina 方法在 EvaLatin 2024 的应用

Nostra Domina at EvaLatin 2024: Improving Latin Polarity Detection  through Data Augmentation

Recent advances in NLP have led to the creation of powerful language models
for many languages including Ancient Greek and Latin. While prior work on
Classical languages unanimously uses BERT, in this work we create four language
models for Ancient Greek that vary along two dimensions to study their
versatility for tasks of interest for Classical languages: we explore (i)
encoder-only and encoder-decoder architectures using RoBERTa and T5 as strong
model types, and create for each of them (ii) a monolingual Ancient Greek and a
multilingual instance that includes Latin and English. We evaluate all models
on morphological and syntactic tasks, including lemmatization, which
demonstrates the added value of T5's decoding abilities. We further define two
probing tasks to investigate the knowledge acquired by models pre-trained on
Classical texts. Our experiments provide the first benchmarking analysis of
existing models of Ancient Greek. Results show that our models provide
significant improvements over the SoTA. The systematic analysis of model types
can inform future research in designing language models for Classical
languages, including the development of novel generative tasks. We make all our
models available as community resources, along with a large curated
pre-training corpus for Ancient Greek, to support the creation of a larger,
comparable model zoo for Classical Philology. Our models and resources are
available at this https URL

本文提出了四种古希腊语言模型，包括单语和多语版本，使用 RoBERTa 和 T5 作为模型类型，基于模型对形态和句法任务进行评估，探讨模型类型对古典语言模型设计的影响。实验结果表明本文设计的模型显著提高了古希腊语言任务的性能，并为未来的研究提供了有用的信息。

探索大型语言模型在古典学中的应用

Exploring Large Language Models for Classical Philology

We present a state-of-the-art neural approach to the unsupervised
reconstruction of ancient word forms. Previous work in this domain used
expectation-maximization to predict simple phonological changes between ancient
word forms and their cognates in modern languages. We extend this work with
neural models that can capture more complicated phonological and morphological
changes. At the same time, we preserve the inductive biases from classical
methods by building monotonic alignment constraints into the model and
deliberately underfitting during the maximization step. We evaluate our
performance on the task of reconstructing Latin from a dataset of cognates
across five Romance languages, achieving a notable reduction in edit distance
from the target word forms compared to previous methods.

本文介绍一种最先进的神经网络方法，用于无监督重建古代词形。通过使用具有单调对齐约束的神经模型，本方法可以捕捉更复杂的语音和形态变化，实现从五种罗曼语言中的藏语重构拉丁文词汇，并显著提高了与以前方法相比的编辑距离减少率。