52 episodes

Everything you need to become an expert in language translation management so you can connect your products and services with people anywhere, today.

The Loc Show Presented by Smartling

    • Business
    • 5.0 • 15 Ratings

Everything you need to become an expert in language translation management so you can connect your products and services with people anywhere, today.

    Reality Series: Machine translation quality estimation

    Reality Series: Machine translation quality estimation

    In this episode of Reality Series, we focus on the myths and realities of machine translation quality estimation, hosted by Mei Zheng, data scientist, and Alex Yanishevsky, director of AI and machine translation.
    Over the past decade, the evolution of machine translation has significantly changed the industry. With that, there are a lot of factors to consider when we look at the quality estimation of machine translation – and this discussion is dives right in, addressing ways that quality is estimated and calculated, and questions like:
    - Does a high score mean high quality?- What role do semantics play in edit rate?- How does a fuzzy match affect translators?- How (and when) should we edit and translate profanities?
    Jump into the Episode[01:39] Busting myth #1: Is 90% quality good enough to publish?[09:58] Busting myth #2: "MT stinks!" [17:51] Busting myth #3: There is one MTQE. [24:13] Q&A
    Sample translations discussed during Myth #2
    en: “Dr. Smith was found guilty of keeping a protected animal in the Atherton Magistrates Court after being charged with removing a scrub python from a resident’s property. She then went through the legal process to appeal the court ruling.”
    zh: 史密斯博士在阿瑟顿地方法院被裁定饲养受保护动物罪,因为她被指控从一个居民的财产中移走一条灌丛蟒蛇。随后,她通过法律程序对法院的裁决提出上诉。
    es: La Dra. Smith fue declarada culpable de tenencia de un animal protegido en el Tribunal de Magistrados de Atherton tras ser acusada de retirar una pitón de matorral de la propiedad de un residente. A continuación recurrió la sentencia judicial.
    ru: Доктор Смит была признана виновной в содержании охраняемого животного в магистратском суде Атертона после того, как ей было предъявлено обвинение в том, что она убрала камышового питона с участка жителя. Затем она прошла через судебный процесс, чтобы обжаловать решение суда.
    Resources and links:
    Smartling Website 

    • 37 min
    Reality Series: Language AI

    Reality Series: Language AI

    In the first episode of our Reality Series, Smartling leaders – Olga Beregovaya (Vice President of AI and Machine Translation), Andrew Batwash (Associate Director of Language Services), and Jack Welde (President) – uncover the myths and realities of LanguageAI.
    Language AI is the topic of the hour for the translation and localization industry. To uncover how it improves business outcomes and where the industry is headed, we explore:
    What is Language AI? Is it the same as Machine Translation?How can we leverage LanguageAI to build efficiencies and cost-savings into our processes?What outcomes have early adopters of LanguageAI seen? What best practices can we learn from them?Where do human translators fit in?Jump into the Episode
    [01:30] About Olga and her background.
    [02:28] About Andrew and his background. 
    [03:41] What is Language AI?
    [08:18] How to leverage Language AI to build more efficient cost and time savings.
    [11:25] What ways are humans part of the translation process and what value do they add?
    [14:22] How companies like Smartling compete with cloud MT providers like Google, Microsoft, and Amazon.
    [17:27] Why companies should be trying to incorporate machine translation.
    [25:17] Skillsets required to stay relevant in the age of AI.
    [28:46] What is MT not good at?
    [36:23] Measures and benchmarks that prove Andrew is having success. 
    [37:07] Is it possible to use context for content that’s localized with MT?
    [41:16] Aside from machine translation, how else is AI being used? 
    [45:05] Final thoughts from Olga and Andrew.

    Resources and links:
    Smartling Website 

    • 46 min
    Chinese: What to Know When Localizing for the Chinese-Speaking Audience

    Chinese: What to Know When Localizing for the Chinese-Speaking Audience

    Junbo joins us to discuss the importance of culture, regional distinctions, and understanding your audience when translating Chinese languages.

    • 26 min
    Italian: What to Know When Localizing for the Italian-Speaking Audience

    Italian: What to Know When Localizing for the Italian-Speaking Audience

    Milena joins us to discuss the challenges of translating Italian to English, tips and tricks, the importance of building your knowledge base and gaining experience as a new translator, and more.

    • 34 min
    Portuguese: What You Should Know When Localizing for the Portuguese-Speaking Audience

    Portuguese: What You Should Know When Localizing for the Portuguese-Speaking Audience

    Sérgio joins us to discuss how best to approach translating for the audience speaking Portuguese.

    • 35 min
    Life-Saving Translations: The Fight for Accurate Information in Marginalized Languages

    Life-Saving Translations: The Fight for Accurate Information in Marginalized Languages

    Stella Hodkin Paris, Head of Language Services of Translators without Borders joins to talk about language gaps around the world and how to overcome the lack of knowledge around marginalized languages.

    • 50 min

Customer Reviews

5.0 out of 5
15 Ratings

15 Ratings

Gazpachisimo ,

New to Localization?

Only one episode so far, but can already tell these casts are going to help novices and experts alike delve deep into the important roles translation and localization play in getting ahead in today’s global economy. Real stories. Practical advice. Cool people.

loc-man ,

All things loc!

The best loc podcast out there!

Jennevive ,

My favorite localization podcast.

Engaging and informative look at the translation and localization space. Looking forward to tuning in weekly!!

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