cd /news/artificial-intelligence/hydraqe-osus-submission-for-the-iwsl… · home topics artificial-intelligence article
[ARTICLE · art-45384] src=aclanthology.org ↗ pub= topic=artificial-intelligence verified=true sentiment=↑ positive

HydraQE: OSU’s Submission for the IWSLT 2026 Speech Translation Metrics Shared Task

Researchers at Ohio State University developed HydraQE, an end-to-end, reference-free quality estimation system for speech translation that outperforms cascaded text-based baselines. Submitted to the IWSLT 2026 shared task, HydraQE uses a Qwen3-ASR backbone with three prediction heads trained on human annotations and pseudo-labels to address data scarcity. The system demonstrates that direct speech translation quality estimation is competitive with cascaded approaches.

read1 min views12 publishedJun 30, 2026
HydraQE: OSU’s Submission for the IWSLT 2026 Speech Translation Metrics Shared Task
Image: Aclanthology (auto-discovered)
Abstract

We present HydraQE, our contribution to the IWSLT 2026 Speech Translation Metrics shared task. HydraQE is an end-to-end, reference-free quality estimation (QE) system for speech translation built on a Qwen3-ASR backbone, which accepts source audio and a translation hypothesis as joint input. Hidden states from all backbone layers are combined via a sparsemax scalar mix, then re-encoded by a bidirectional Transformer for full cross-modal interaction. To address the scarcity of human-annotated speech translation data, three independent prediction heads are trained on complementary supervision signals: human direct assessment (DA) annotations, MetricX-24 pseudo-labels, and xCOMET pseudo-labels. We train on a combination of synthetically corrupted examples and silver pseudo-labeled machine translation outputs, using a curriculum that begins on synthetic and silver data and gradually shifts toward human-annotated examples. HydraQE outperforms cascaded text-based baselines and prior direct speech QE systems, demonstrating that end-to-end speech translation QE is competitive with cascaded approaches.- Anthology ID:

- 2026.iwslt-1.37
- Volume:
[Proceedings of the 23rd International Conference on Spoken Language Translation (IWSLT 2026)](/volumes/2026.iwslt-1/)- Month:
  • July
  • Year:
  • 2026
- Address:
- San Diego, USA (in-person and online)
- Editors:

Elizabeth Salesky,Antonios Anastasopoulos,Matteo Negri,Marcello Federico- Venues:

[IWSLT](/venues/iwslt/)|[WS](/venues/ws/)- SIG:
[SIGSLT](/sigs/sigslt/)- Publisher:
  • Association for Computational Linguistics
- Note:
- Pages:
  • 323–331
- Language:
- URL:
[https://aclanthology.org/2026.iwslt-1.37/](https://aclanthology.org/2026.iwslt-1.37/)- DOI:
- Cite (ACL):
[HydraQE: OSU’s Submission for the IWSLT 2026 Speech Translation Metrics Shared Task](https://aclanthology.org/2026.iwslt-1.37/)(Krahn & Fosler-Lussier, IWSLT 2026)- PDF:
[https://aclanthology.org/2026.iwslt-1.37.pdf](https://aclanthology.org/2026.iwslt-1.37.pdf)
── more in #artificial-intelligence 4 stories · sorted by recency
── more on @ohio state university 3 stories trending now
sponsored brought to you by zahid.host 4,200+ EU-deployed projects
reading about agents? ship yours in a single git push.

Run your AI side-project on zahid.host

EU-based hosting, git-push deploys, automatic HTTPS, no cold starts. Free tier with a custom domain — perfect for shipping the agent you just read about.

$git push zahid main
Live at https://your-agent.zahid.host
Get free account → Pricing
from €0/mo · no card required
LIVE [news/hydraqe-osus-submiss…] indexed:0 read:1min 2026-06-30 ·