# MockConf: A Student Interpretation Dataset: Analysis, Word- and Span-level Alignment and Baselines

arXiv: https://arxiv.org/abs/2506.04848 · HTML: https://mlsanity.com/paper/2506.04848/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 72 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 13 of 13)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings](https://mlsanity.com/paper/2004.08728/) (2020)
- [GEMBA-MQM: Detecting Translation Quality Error Spans with GPT-4](https://mlsanity.com/paper/2310.13988/) (2023)
- [Contrastive Learning for Many-to-many Multilingual Neural Machine Translation](https://mlsanity.com/paper/2105.09501/) (2021)
- [Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Better](https://mlsanity.com/paper/2212.08597/) (2022)
- [MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation Evaluators](https://mlsanity.com/paper/2409.14335/) (2024)
- [MuST-Cinema: a Speech-to-Subtitles corpus](https://mlsanity.com/paper/2002.10829/) (2020)
- [JESC: Japanese-English Subtitle Corpus](https://mlsanity.com/paper/1710.10639/) (2017)
- [Lost in Interpreting: Speech Translation from Source or Interpreter?](https://mlsanity.com/paper/2106.09343/) (2021)
- [Automatic Estimation of Simultaneous Interpreter Performance](https://mlsanity.com/paper/1805.04016/) (2018)
- [An Automatic Quality Metric for Evaluating Simultaneous Interpretation](https://mlsanity.com/paper/2407.06650/) (2024)

## Built on by (most cited first, 1 of 1)

- [The GDN-CC Dataset: Automatic Corpus Clarification for AI-enhanced Democratic Citizen Consultations](https://mlsanity.com/paper/2601.14944/) (2026)

## Abstract (arXiv)

In simultaneous interpreting, an interpreter renders a source speech into another language with a very short lag, much sooner than sentences are finished. In order to understand and later reproduce this dynamic and complex task automatically, we need dedicated datasets and tools for analysis, monitoring, and evaluation, such as parallel speech corpora, and tools for their automatic annotation. Existing parallel corpora of translated texts and associated alignment algorithms hardly fill this gap, as they fail to model long-range interactions between speech segments or specific types of divergences (e.g., shortening, simplification, functional generalization) between the original and interpreted speeches. In this work, we introduce MockConf, a student interpreting dataset that was collected from Mock Conferences run as part of the students' curriculum. This dataset contains 7 hours of recordings in 5 European languages, transcribed and aligned at the level of spans and words. We further implement and release InterAlign, a modern web-based annotation tool for parallel word and span annotations on long inputs, suitable for aligning simultaneous interpreting. We propose metrics for the evaluation and a baseline for automatic alignment. Dataset and tools are released to the community.