Diptesh Kanojia

| 27 October: World Day for Audiovisual Heritage 2025 🎬 | Today we celebrate the sounds and images that tell humanity’s story and the professionals who ensure those stories transcend language and culture #WorldDayForAudiovisualHeritage #AudiovisualTranslation #Accessibility

💡 I'm happy to share our new article on how language & communication barriers impact mental healthcare for migrants across Europe — based on survey responses from over 600 health & social care professionals in 9 countries buff.ly/9nyU73r #1nt #health #mentalhealth

Deadline extended to 6th August AoE! :)

Organizers are happy to help with any questions. 🙂 Website with all details and contacts: www2.statmt.org/wmt25/mteval-s…

📐Task 3: Quality-informed segment-level error correction Automatically post-edit machine-translated text using quality annotations to generate minimal and accurate corrections. Description: www2.statmt.org/wmt25/mteval-s… Submission platform: codabench.org/competitions/8…

📐Task 2: Span-level error detection Identify and locate translation errors within each segment (start/end indices) and classify their severity. Description: www2.statmt.org/wmt25/mteval-s… Submission platform: codabench.org/competitions/9…

📐Task 1: Segment-level quality score prediction Predict a quality score for each source–target segment pair, using document-level context and either ESA or MQM annotations. Description: www2.statmt.org/wmt25/mteval-s… Submission platform: codabench.org/competitions/9…

The 2025 MT Evaluation shared task brings together the strengths of the previous Metrics and Quality Estimation tasks under a single, unified evaluation framework. The following tasks are now open (deadline July 31st but participation has never been easier 🙂)

Good luck to all participants! We are incredibly excited to see the innovative solutions you've developed. For full details, baselines, and data formats, visit the official task page. See you in Suzhou! #WMT25 #SharedTask #ComputationalLinguistics #nlproc

📢 Test Set RELEASED! 🚀 The test set for the #WMT25 Shared Task on QE-informed Segment-level Error Correction is now LIVE! It's time to put your MT error correction / APE methods to the test. Let's see how well they can correct machine translation! #NLProc #MT #WMT2025

🔗 Get the Data & Submit: 📥 Download the Test Set: www2.statmt.org/wmt25/mteval-s… (Link is in the "TEST DATA" section) 🏆 Submit on Codabench: codabench.org/competitions/8…

📊 Evaluation: Systems will be ranked on two key metrics: 1️⃣ DeltaCOMET: Primary metric measuring the raw quality improvement over the original MT. 2️⃣ Gain-to-Edit Ratio: DeltaCOMET divided by TER, rewarding systems that are not just effective, but also efficient. #MTeval

Machine Translation is my first and final love. Every single work I do has some flavor of Machine Translation to it. Machine Translation is the best test bed for any sequence to sequence neural architecture. So it's best you read the book on NMT by the OG MT teacher Prof Philipp Koehn of @JHUCompSci. I can't recommend this enough. arxiv.org/abs/1709.07809

📢 Presenting IndicSeamless: A Speech Translation Model for Indian Languages 🎙️🌍 IndicSeamless is a speech translation model fine-tuned from SeamlessM4Tv2-large on 13 Indian languages. Trained on a curated subset of BhasaAnuvaad, the largest open-source Speech Translation dataset for Indian languages and English, this model enhances translation quality across diverse linguistic contexts. 🔹 Why IndicSeamless? Indian languages pose unique challenges in speech translation due to their linguistic diversity, resource limitations, and speech variations. IndicSeamless addresses these challenges by improving accuracy, fluency, and contextual relevance across multiple languages. 🔹 Key Features: ✅ Fine-tuned on a diverse subset of BhasaAnuvaad 🗂️ ✅ Handles spontaneous and read speech effectively 🗣️📖 ✅ Robust to noise variations and diverse accents 🎧 ✅ Optimized for real-world deployment in speech applications 🔹 Resources: 🔗 Demo: huggingface.co/spaces/ai4bhar… 🔗 Model: huggingface.co/ai4bharat/indi… 🔗 Paper: arxiv.org/abs/2411.04699 This represents a significant step forward in multilingual speech translation for the Indian subcontinent. Please try out the demo hosted on @huggingface who have kindly allotted us an A100 GPU! Thanks, @reach_vb for the support!

Prof. Pushpak Bhattacharyya, in conversation with @EconomicTimes, advocates for trinity models—smaller, cost-effective AI models tailored to India’s diverse languages, domains, and tasks. Link: economictimes.indiatimes.com/tech/artificia… #CFILT #NLP #AI #LLM