Unbabel Tackles Metric Bias in AI Translation – slator.com
In a March 11, 2025 paper, Unbabel introduced MINTADJUST, a method for more accurate and reliable machine translation (MT) evaluation. MINTADJUST addresses metric interference (MINT), a phenomenon where using the same or related metrics for both model optimization and evaluation leads to over-optimistic performance estimates. The researchers identified two scenarios where MINT commonly occurs and […]
Google, Unbabel Expand Key AI Translation Benchmark to 55 Languages with WMT24++ – slator.com
Researchers from Google and Unbabel have unveiled WMT24++, a major expansion of the WMT24 machine translation (MT) benchmark, extending its language coverage from 9 to 55 languages and dialects. The dataset now includes human-written reference translations and post-edits for 46 additional languages, as well as new post-edits of the references for 8 of the original […]
Researchers Present DOLFIN, a New Test Set for AI Translation for Financial Content – slator.com
On February 5, 2025, a team of researchers from Grenoble Alpes University and Lingua Custodia, a France-based company specializing in AI and natural language processing (NLP) for the finance sector, introduced DOLFIN, a new test set designed to evaluate document-level machine translation (MT) in the financial domain. The researchers say that the financial domain presents […]
‘EmoDubber’ to Deliver Emotional Control in AI Dubbing, Researchers Hope – slator.com
In a December 12, 2024 paper, researchers from the Chinese Academy of Sciences, Macquarie University, Peking University, and the University of Adelaide proposed EmoDubber, an AI dubbing system that offers high-quality lip synchronization, clear pronunciation, and dynamic control over emotion type and intensity. Traditional AI dubbing systems have struggled with synchronizing lip movements to audio […]
How Apple Wants to Fix Hallucinations in AI Translation – slator.com
In a January 28, 2025, paper Rajen Chatterjee and Sarthak Garg from Apple, along with Zilu Tang from Boston University, presented a framework for mitigating translation hallucinations in large language models (LLMs). According to the researchers, “this is among the first works to demonstrate how to mitigate translation hallucination in LLMs.” They explained that LLM-based […]