EPIC-EuroParl-UdS: Information-Theoretic Perspectives on Translation and Interpreting

TL;DR

This paper presents an updated multilingual corpus of European Parliament speeches with enhanced annotations, supporting information-theoretic research on translation, interpretation, and language variation, and demonstrates its utility through a filler prediction study.

Contribution

It introduces a refined, multi-layered corpus combining spoken and written European Parliament data with new annotations, enabling advanced research in translation and interpretation analysis.

Findings

Validated the integrity of the spoken data after updates

Evaluated GPT-2 and MT models for filler prediction in interpreting

Demonstrated the corpus's utility for language variation studies

Abstract

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.