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

arXiv cs.CL / 3/11/2026

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Key Points

  • The paper presents an updated and merged version of the EPIC-UdS (spoken) and EuroParl-UdS (written) English-German corpora, including original European Parliament speeches and their translations and interpretations.
  • It corrects previous metadata and text errors, enhances linguistic annotations, and introduces new layers such as word alignment and word-level surprisal indices.
  • The combined corpus supports research on information-theoretic analysis of language variation, including comparative studies between spoken and written modes, disfluency analysis, and translationese.
  • A new study validates the rebuilt spoken data and assesses probabilistic measures from GPT-2 and machine translation models in predicting filler particles during interpreting.
  • The resource and findings aim to advance translation and interpreting research through computational and probabilistic frameworks.

Computer Science > Computation and Language

arXiv:2603.09785 (cs)
[Submitted on 10 Mar 2026]

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

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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.
Comments:
Subjects: Computation and Language (cs.CL)
ACM classes: I.2.7; G.3; H.3.1; J.5
Cite as: arXiv:2603.09785 [cs.CL]
  (or arXiv:2603.09785v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2603.09785
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arXiv-issued DOI via DataCite

Submission history

From: Maria Kunilovskaya [view email]
[v1] Tue, 10 Mar 2026 15:20:40 UTC (1,393 KB)
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