TAUS Estimate API officially in production, unlocking efficiency and cost-savings

TAUS正式评估API投入生产,释放效率和成本节约

2023-11-09 07:31 multilingual

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Amsterdam, 9 November, 2023 – TAUS, a leading provider of data-services and human-powered language data for AI solutions, has gone officially in production with their DeMT™ Estimate API. Work on this quality estimation feature started in October 2022. After a period of scoping, building the infrastructure, training the models and thorough testing with our early adaptors, TAUS is proud to announce that the Estimate API is now out of beta and into production. Quality estimation plays a pivotal role in ensuring the accuracy and reliability of machine-translated content. The model thoroughly assesses and compares the source and target segments in order to quantify the quality of the output. Through this, organizations are able to gauge the reliability of the MT output, reducing the dependence on human post-editing and decreasing their time-to-market as well as their costs. Quality estimation involves advanced neural modeling techniques that analyze various aspects of a translation, such as fluency, adequacy, and adherence to domain-specific terminology. “The official production launch of our Quality Estimate API marks a significant milestone in streamlining modern translation workflows, making them more efficient and cost-effective for organizations seeking to bridge language barriers and reach global audiences”, says Amir Kamran, Solution Architect at TAUS. “With our API, businesses can integrate state-of-the-art quality estimation into their applications, gaining real-time insights into their quality, and ultimately, delivering high-quality multilingual experiences to their customers.” The Estimate API is actively processing millions of characters each day, serving clients from both enterprise and LSP domains. The out-of-the-box generic models, pre-trained in over a hundred languages, are already well-suited for a number of use cases. However, the team is dedicated to ongoing enhancements through extensive model training, fine-tuning and exploring new QE frameworks, particularly for languages with limited representation. Looking forward, TAUS is also developing improved metrics to obtain not just semantic, but also grammatical and linguistic insights. Furthermore, TAUS offers model customization to ensure that the quality scores are well aligned with the distinctive content, style and quality expectations of each client. “At Milengo, we initiated collaboration with TAUS to evaluate their MTQE technology. Following a comprehensive series of test projects, we were notably impressed by the seamless integration of MTQE technology into our existing workflows and computer-assisted translation (CAT) tools. Consequently, we have made the strategic decision to procure a bundled package, ensuring its continued utilization across our project spectrum”, says Sarita Vásquez, Machine Translation Product Owner at Milengo. “Our intention is to harness this cutting-edge technology to systematically appraise MT quality, thereby enhancing our capability to identify and address MT-related challenges and enhance productivity.”
Amsterdam,2023年11月9日——人工智能解决方案的数据服务和人工语言数据的领先提供商TAUS已经正式投入生产他们的DeMT™评估API。这项质量评估功能的工作始于2022年10月。经过一段时间的范围界定、基础设施构建、模型训练和对我们早期适配器的彻底测试,TAUS自豪地宣布,Estimate API现已退出测试并投入生产。 质量评估在确保机器翻译内容的准确性和可靠性方面起着关键作用。该模型彻底评估和比较来源和目标部分,以量化产出的质量。通过这种方式,组织能够衡量机器翻译输出的可靠性,减少对人工后期编辑的依赖,缩短上市时间和成本。质量评估涉及先进的神经建模技术,分析翻译的各个方面,如流畅性、充分性和对特定领域术语的遵守。 TAUS的解决方案架构师Amir Kamran表示:“我们的Quality Estimate API正式推出,标志着简化现代翻译工作流程的一个重要里程碑,使其对于寻求跨越语言障碍和接触全球受众的组织来说更加高效和经济。”“通过我们的API,企业可以将最先进的质量评估集成到他们的应用程序中,实时了解他们的质量,最终为他们的客户提供高质量的多语言体验。” Estimate API每天主动处理数百万个字符,为来自企业和LSP域的客户端提供服务。开箱即用的通用模型,经过一百多种语言的预训练,已经非常适合许多用例。然而,该团队致力于通过广泛的模型训练、微调和探索新的QE框架来进行持续的增强,特别是对于代表性有限的语言。展望未来,TAUS还在开发改进的指标,不仅获得语义,还获得语法和语言见解。此外,TAUS提供模型定制,以确保质量分数与每个客户独特的内容、风格和质量期望保持一致。 “在Milengo,我们开始与TAUS合作,评估他们的MTQE技术。在一系列全面的测试项目之后,MTQE技术与我们现有工作流程和计算机辅助翻译(CAT)工具的无缝集成给我们留下了深刻的印象。因此,我们做出了购买捆绑包的战略决策,以确保它在我们的项目范围内持续使用”,Milengo机器翻译产品负责人Sarita V á squez说。“我们的目的是利用这一尖端技术系统地评估机器翻译的质量,从而提高我们识别和应对机器翻译相关挑战的能力,并提高生产率。”

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