For 2026-04, delivered a targeted update to the IWSLT 2026 Metrics Submission Guidelines within IWSLT/IWSLThub.io.git. The update clarifies test data requirements, submission format, and submission deadlines, and was implemented via a focused commit. The work reduces ambiguity, supports accurate metric capture, and aligns with project timelines.

March 2026: Delivered Metrics Task Timeline Adjustment in IWSLThub.io.git. Postponed evaluation period and system paper submission deadlines; updated the overall project timeline. No major bugs fixed this month. This work improves milestone predictability and cross-team planning for the Metrics Task.

January 2026: Focused on improving evaluation transparency for IWSLThub.io.git by delivering Metrics Shared Task Test Set Information for IWLST 2026, including human annotations for en-de and en-zh. Updated docs in IWSLT/IWSLThub.io.git (commit eb9be11cf23bdfa49155f820c64fa651b265e6b9). No major bugs fixed; maintenance centered on documentation and task clarity. Impact: clearer evaluation guidance, improved benchmarking reliability, and faster onboarding for researchers and developers. Skills demonstrated: documentation discipline, task scoping, and cross-team alignment.

December 2025 monthly focus: delivering transparency and measurement capabilities for the shared task training/evaluation pipeline in IWSLThub.io.git, driving participant clarity and data-driven improvements.

In Nov 2025, delivered QE-focused evaluation enhancement for Speech Translation within IWSLT/IWSLThub.io.git. Updated metrics task description to emphasize Quality Estimation, robust evaluation methods, and a clarified submission workflow. This work enhances measurement reliability and supports a more business-aligned release process.

Oct 2025: Focused on expanding noisy data infrastructure and evaluation tooling for the hearing2translate project. Delivered ingestion support for the noisy_fleurs_babble dataset across en-nl and en-pt and added analysis tools to compare clean vs noisy conditions, enabling quantified impact on translation quality. No major bugs fixed this month. Impact: broader experimental coverage, improved robustness insights, and a foundation for more noise-aware MT models. Technologies/skills demonstrated: dataset curation, data pipeline extension, analysis scripting, CSV generation for reproducibility, and multilingual evaluation readiness.