This paper introduces an updated and combined version of the bidirectional English-German EPIC-UdS (spoken) and EuroParl-UdS (written) corpora containing original European Parliament speeches as well as their translations and interpretations. The new version corrects metadata and text errors identified through previous use, refines the content, updates linguistic annotations, and adds new layers, including word alignment and word-level surprisal indices. The combined resource is designed to support research using information-theoretic approaches to language variation, particularly studies comparing written and spoken modes, and examining disfluencies in speech, as well as traditional translationese studies, including parallel (source vs. target) and comparable (original vs. translated) analyses. The paper outlines the updates introduced in this release, summarises previous results based on the corpus, and presents a new illustrative study. The study validates the integrity of the rebuilt spoken data and evaluates probabilistic measures derived from base and fine-tuned GPT-2 and machine translation models on the task of filler particles prediction in interpreting.

翻译：本文介绍了双向英语-德语EPIC-UdS（口语）语料库和EuroParl-UdS（书面）语料库的更新合并版本，该版本包含欧洲议会的原始演讲及其笔译与口译内容。新版修正了先前使用中发现的元数据和文本错误，优化了内容，更新了语言学标注，并新增了包括词对齐和词级惊异度指数在内的多个标注层。该合并资源旨在支持采用信息论方法研究语言变体，特别是比较书面与口语模式、考察言语非流利现象的研究，以及传统的翻译语言特征研究，包括平行（源语与目标语）分析和类比（原创与翻译）分析。本文概述了此版本中引入的更新，总结了基于该语料库的先前研究成果，并展示了一项新的示例性研究。该研究验证了重建口语数据的完整性，并评估了基于基础及微调GPT-2模型与机器翻译模型所推导的概率度量在口译填充词预测任务上的表现。