We introduce a dataset on political orientation and power position
identification. The dataset is derived from ParlaMint, a set of comparable
corpora of transcribed parliamentary speeches from 29 national and regional
parliaments. We introduce the dataset, provide the reasoning behind some of the
choices during its creation, present statistics on the dataset, and, using a
simple classifier, some baseline results on predicting political orientation on
the left-to-right axis, and on power position identification, i.e.,
distinguishing between the speeches delivered by governing coalition party
members from those of opposition party members.

介绍了一个关于政治取向和权力地位识别的数据集，该数据集从 ParlaMint 中派生而来，ParlaMint 是 29 个国家和地区议会的一组可比较的国会演讲语料库。我们介绍了该数据集，解释了在创建过程中的一些选择原则，在数据集上提供了统计数据，并利用一个简单的分类器对政治取向在左右轴线上的预测结果以及权力地位识别的基线结果进行了介绍，即区分执政联盟党派成员和反对党派成员发表的演讲。

议会中的多语言权力与意识形态识别：参考数据集与简单基线

Multilingual Power and Ideology Identification in the Parliament: a  Reference Dataset and Simple Baselines

Election speeches play an integral role in communicating the vision and
mission of the candidates. From lofty promises to mud-slinging, the electoral
candidate accounts for all. However, there remains an open question about what
exactly wins over the voters. In this work, we used state-of-the-art natural
language processing methods to study the speeches and sentiments of the
Republican candidate, Donald Trump, and Democratic candidate, Joe Biden,
fighting for the 2020 US Presidential election. Comparing the racial dichotomy
of the United States, we analyze what led to the victory and defeat of the
different candidates. We believe this work will inform the election campaigning
strategy and provide a basis for communicating to diverse crowds.

本文利用最先进的自然语言处理方法，对 2020 年美国总统选举中的两位候选人特朗普和拜登的竞选演讲与情感进行了研究和分析，比较了美国的种族二元论，分析了不同候选人胜利和失败的原因，希望该研究能够为竞选策略和信息传递提供基础。