Semantic relations among entities are a widely accepted method for relation
extraction. PromptORE (Prompt-based Open Relation Extraction) was designed to
improve relation extraction with Large Language Models on generalistic
documents. However, it is less effective when applied to historical documents,
in languages other than English. In this study, we introduce an adaptation of
PromptORE to extract relations from specialized documents, namely digital
transcripts of trials from the Spanish Inquisition. Our approach involves
fine-tuning transformer models with their pretraining objective on the data
they will perform inference. We refer to this process as "biasing". Our Biased
PromptORE addresses complex entity placements and genderism that occur in
Spanish texts. We solve these issues by prompt engineering. We evaluate our
method using Encoder-like models, corroborating our findings with experts'
assessments. Additionally, we evaluate the performance using a binomial
classification benchmark. Our results show a substantial improvement in
accuracy -up to a 50% improvement with our Biased PromptORE models in
comparison to the baseline models using standard PromptORE.

通过对西班牙宗教审判数字抄本数据进行微调，我们提出了一种改进的 PromptORE 方法（即对大型语言模型进行训练，以从专业文本中提取关系）。我们通过 Prompt 工程解决了西班牙语文本中复杂的实体定位和性别问题，得到了显著的准确性提升 - 相对于标准 PromptORE 基准模型，我们的 Biased PromptORE 模型准确率提高了 50%。

适应现代历史：从 16 世纪西班牙君主文档中提取信息的 PromptORE 调整

Adapting PromptORE for Modern History: Information Extraction from  Hispanic Monarchy Documents of the XVIth Century

Probably the most important problem in machine learning is the preliminary
biasing of a learner's hypothesis space so that it is small enough to ensure
good generalisation from reasonable training sets, yet large enough that it
contains a good solution to the problem being learnt. In this paper a mechanism
for {\em automatically} learning or biasing the learner's hypothesis space is
introduced. It works by first learning an appropriate {\em internal
representation} for a learning environment and then using that representation
to bias the learner's hypothesis space for the learning of future tasks drawn
from the same environment.
An internal representation must be learnt by sampling from {\em many similar
tasks}, not just a single task as occurs in ordinary machine learning. It is
proved that the number of examples $m$ {\em per task} required to ensure good
generalisation from a representation learner obeys $m = O(a+b/n)$ where $n$ is
the number of tasks being learnt and $a$ and $b$ are constants. If the tasks
are learnt independently ({\em i.e.} without a common representation) then
$m=O(a+b)$. It is argued that for learning environments such as speech and
character recognition $b\gg a$ and hence representation learning in these
environments can potentially yield a drastic reduction in the number of
examples required per task. It is also proved that if $n = O(b)$ (with
$m=O(a+b/n)$) then the representation learnt will be good for learning novel
tasks from the same environment, and that the number of examples required to
generalise well on a novel task will be reduced to $O(a)$ (as opposed to
$O(a+b)$ if no representation is used).
It is shown that gradient descent can be used to train neural network
representations and experiment results are reported providing strong
qualitative support for the theoretical results.

本文介绍一种通过学习或有偏移地学习学习者的假设空间的机制，重点解决了在机器学习中如何预先调节学习者假设空间的问题。文章通过采样学习环境的多个类似任务来学习内部表示，证明不同的学习任务可以共享同一个内部表示从而达到更好的泛化效果。文章还展示了使用梯度下降算法来训练神经网络内部表示的有效性。