Large Language Models Beat Commercial MT Models for Arabic Dialects, Research Finds
New research shows that large language models are better translators of Arabic dialects than commercial machine translation systems but remain far from perfect.
*New* Slator Pro Guide: Scaling an LSP Key Account — Growing Small Clients into Key Accounts
Maria Stasimioti
Research Analyst at Slator. Based in Athens, Greece.
China’s Top Tech Firm Proposes New Way to Improve CAT Tool Word Auto-Completion
Researchers from Shanghai Jiao Tong University and Tencent AI Lab introduce a method to elevate word-level auto-completion through machine translation, with experimental results showcasing noteworthy enhancements.
Here Are the Best-Performing Machine Translation Evaluation Metrics
New research reveals the best-performing machine translation evaluation metrics, identifies major challenges in metrics development, and suggests improvements.
New Research Explores How to Simplify Translation Between Related Languages
A study introduces an approach to streamline translation between related languages, with the goal of enhancing trade efficiency and strengthening social connections, particularly in regions with related languages.
Are Large Language Models Good at Translation Post-Editing?
Microsoft Azure AI researchers explore the potential of large language models for automatic post-editing and find that LLMs are good but not great at it.
Slator Pro Guide: Scaling an LSP Key Account
The Slator Pro Guide provides key insights on how LSPs nurture client accounts, expand share-of-wallet, and show localization RoI.
Low-Resource Language Machine Translation Can Compromise Large Language Model Safety
Brown University researchers reveal an issue with AI safety mechanisms in large language models involving low-resource languages.
Large Language Models Are Really Good at Data Cleaning, Research Finds
A study demonstrates the ability of large language models to remove noise from datasets and underscores their potential for data cleaning.
How Effective Are Large Language Models in Low-Resource Language Translation
Carnegie Mellon University researchers explore LLM effectiveness across 204 languages revealing their output limitations for low-resource languages.
The Case for Contextual Evaluation in Measuring LLM Translation Quality
At SlatorCon Zurich, Dr. Sheila Castilho emphasizes the significance of contextual evaluation in assessing large language models and the need for a more rigorous evaluation approach.
Inside the Potential of Large Language Models in Localization at SlatorCon Zurich 2023
Panelists from ServiceNow, LanguageWire, Busch Vacuum Solutions weighed the risks and opportunities of employing large language models in practical enterprise localization workflows.
How WIPO Fully Automated Transcription and Translation of Meeting Transcripts
The World Intellectual Property Organization unveiled its in-house solution designed to generate conference meeting transcripts and machine translations.
Can Large Language Models Do Simultaneous Machine Translation?
Monash University researchers show that large language models can do real-time machine translation and propose new ways for model fine-tuning.
Google Debuts New Multilingual Machine Translation Model For Low Resource Languages
Google created a new dataset for machine translation and multilingual NLP tasks across 400 languages and released a high-performing multilingual MT model trained on this data.
DeepMind’s Path to Better Large Language Models Runs Through Machine Translation
DeepMind introduces a new method to improve the quality of large language models. Researchers choose machine translation as a use case to show how well the new approach works.
Top Language AI Researchers Propose New Way to Auto-Evaluate Machine Translation
Language AI researchers show that fine-tuning large language models with fine-grained human judgment data boosts machine translation evaluation.
Study Shows Improved Quality Estimation via Fine-Tuning LLMs with Post-Editing Data
Logrus Global and the University of Manchester showcase that fine-tuning LLMs on historical post-editing data can pinpoint segments that require editing and predict translation quality.
Researchers Propose New Approach to Many-to-Many Spoken Language Translation
KAIST and Google DeepMind researchers propose training a single model using Unit-to-Unit Translation to achieve seamless cross-language communication.
ADAPT Enhances Explainable Neural Machine Translation with adaptNMT
ADAPT researchers introduce adaptNMT, an innovative open-source application designed to simplify the development and deployment of machine translation models.
Ghent University’s MATEO Project Improves Access to Machine Translation Evaluation
MATEO aims to open up machine translation evaluation, making it accessible to more stakeholders and facilitating research, education, and critical evaluation of machine translation.
Research Shows How to Replicate ‘Translator Style’ in Literary Machine Translation
Boğaziçi University researchers reveal the potential of customizing MT systems to replicate a translator's style, leading to literary translations that mirror the translator's unique style.
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