EPIC-EuroParl-UdS: Information-Theoretic Perspectives on Translation and Interpreting
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 46 references that are in the map. First posted on arXiv 10 Mar 2026.
Builds on
6 of 46 references.
References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
- 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 719
- 2021Word Alignment by Fine-tuning Embeddings on Parallel Corporacited by 186
- 2022Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?cited by 111
- 2023Testing the Predictions of Surprisal Theory in 11 Languagescited by 75
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Citing papers per year: 2026: 1.
1 paper.