MMIA English–Ukrainian Simultaneous Interpreting Dataset with Cognitive Indices (2014–2025)
Abstract
This dataset contains 2,000 annotated segments of English–Ukrainian and Ukrainian–English simultaneous interpreting prepared within the framework of the Multidimensional Model of Interpreting (МБІУП). The material represents short interpreting segments reflecting discourse typical for diplomatic, institutional and public communication contexts between 2014 and 2025. Each segment includes aligned source and interpreted utterances together with a set of analytical indices designed to capture cognitive and communicative aspects of the interpreting process. These include measures related to ear–voice span (EVS), pause ratio, self-repair activity, compression rate and several model-based indices describing semantic reproduction (IR), cognitive load balance (BKN), discursive adaptation (IDA), cultural equivalence (KCE), technological conditions of interpreting (KTI) and communicative sensitivity in security-related discourse (IBS). The dataset also contains an integrated quality indicator (Q) combining several of these parameters. The data were structured as interpreting segments grouped into episodes representing short fragments of interpreted discourse. The aim of the dataset is to provide a structured empirical basis for the analysis of cognitive processes in simultaneous interpreting and to support corpus-based research in interpreting studies. The dataset is released as FAIR data and is provided in several formats (XLSX, CSV, TSV) together with methodological documentation, metadata and a statistical overview of the corpus. It can be used for research in interpreting studies, cognitive translation studies, corpus linguistics and related fields.
Fields of Study & Subjects
Translation Studies and Practices
Natural Language Processing Techniques
Subtitles and Audiovisual Media
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 11 March 2026
- Language
- EN
- Document type
- Dataset