maikezu/iwslt2026-metrics-shared-train-dev
Metrics-Shared Task IWSLT 2026 Train and Dev Set This dataset contains the train and dev sets for the Speech Translation Metrics Shared Task at IWSLT 2026. More details about the shared task can be found on the IWSLT website. The dataset is primarily designed for research in speech translation quality estimation. Task Goal Given a speech sample and a system-generated translation, the goal is to estimate a score that reflects the translation quality.… See the full description on the dataset page: https://huggingface.co/datasets/maikezu/iwslt2026-metrics-shared-train-dev.
Metrics-Shared Task IWSLT 2026 Train and Dev Set
This dataset contains the train and dev sets for the Speech Translation Metrics Shared Task at IWSLT 2026. More details about the shared task can be found on the IWSLT website.
The dataset is primarily designed for research in speech translation quality estimation.
Task Goal
Given a speech sample and a system-generated translation, the goal is to estimate a score that reflects the translation quality.
Dataset Splits
train
Contains a mix of:
- IWSLT 2023 human annotations (details IWSLT 2023)
- Previous and following segments can be inferred from the
doc_idfeature. - Human evaluators considered context of one previous and one following segment.
- WMT 2024 human annotations (details WMT 2024)
- Evaluated on segmented audio. The information on previous/following segments is not available.
- WMT 2025 human annotations (details WMT 2025)
- Evaluated on segmented audio. The information on previous/following segments is not available.
train_synthetic
Contains:
- SpeechQE data (details SpeechQE)
- Based on Common Voice.
- Automatically annotated (synthetic scores).
dev
Contains:
- IWSLT 2025 ACL Talks human annotations (details IWSLT 2025)
- Previous and following segments can be inferred from the
doc_idfeature. - Human evaluators considered context of one previous and one following segment.
Features
Citation
@inproceedings{adelani-etal-2026-iwslt,
title = {Speech Translation and Metrics in 2026: Findings of the IWSLT Campaign},
author = {
Adelani, David Ifeoluwa
and Anastasopoulos, Antonios
and Agostinelli, Victor
and Bentivogli, Luisa
and Bojar, Ond{\v{r}}ej
and Brati{\`e}res, Sebastien
and Carpuat, Marine
and Cattoni, Roldano
and Cettolo, Mauro
and Chen, Lizhong
and Federico, Marcello
and Gaido, Marco
and Gupta, Mahendra
and Han, HyoJung
and Hatami, Ali
and Javorsk{\'y}, David
and Jeon, Yejin
and Kasztelnik, Marek
and Liu, Danni
and Luu, Nam
and Ma, Min
and Mach{\'a}{\v{c}}ek, Dominik
and Maltais, Marie
and Matusov, Evgeny
and Maurya, Chandresh Kumar
and McCrae, John P.
and Moslem, Yasmin
and Nakamura, Satoshi
and Negri, Matteo
and Niehues, Jan
and Ojha, Atul Kr.
and Ouyang, Siqi
and Papi, Sara
and Pol{\'a}k, Peter
and Retkowski, Fabian
and Savoldi, Beatrice
and Sikasote, Claytone
and Sperber, Matthias
and St{\"u}ker, Sebastian
and Sudoh, Katsuhito
and Turchi, Marco
and Waibel, Alex
and Wilken, Patrick
and Zouhar, Vil{\'e}m
and Z{\"u}fle, Maike
},
booktitle = {Proceedings of the 23rd International Conference on Spoken Language Translation (IWSLT 2026)},
year = {2026},
address = "San Diego, California, US",
publisher = "Association for Computational Linguistics",
}Dataset Card Contact
Maike Züfle @maikezu
