In this session, we will offer several quick and dynamic presentations covering topics and ideas too interesting to ignore!

Drink Your Own Champagne — We Spent One Year in Our Customers’ Shoes — Francesca Sorrentino (Phrase)
As a localization technology provider, we excel in helping our customers streamline their localization processes using the best technology and our expert resources. But to really understand the opportunities and challenges faced by our customers we decided to “drink our own champagne”, and run our own comprehensive internal localization program using our own technology across the business to gain firsthand insights and walk in our customers’ shoes. We’ll share the challenges we faced navigating legacy processes and managing change, and how we successfully implemented new workflows and automation using the very latest tech across marketing, product, customer success, and sales.
Takeaways: Attendees will get insights on the challenges that even experts who have the latest technology face when providing localization services; get an understanding of how we organized our internal localization program using Phrase products; get a totally unique perspective from a solution provider that has access to all the very latest tech capabilities available

Virtual Linguistic Barriers: A Case for Language Accessibility in the Metaverse — Marina Ilara (Terra Translations)
XR companies are increasingly aware of accessibility concerns for the visually or hearing impaired, but they seem to be leaving a much broader audience on the other side of a monolingual linguistic barrier. In this session, we will explore the current state of language accessibility in the metaverse, analyze potential reasons behind it, and propose solutions to create a more diverse space with access for all.
Takeaways: In this session, attendees will: Learn about the current state of linguistic accessibility in the metaverse and get a better understanding of what the language industry can do to help build a more inclusive metaverse.

Quality Prediction: AI for More Efficient Post-editing — Adam Bittlingmayer (ModelFront)
Quality prediction is AI that predicts the quality of a machine translation, or even a human translation. The highest-value use case of quality prediction is safely skipping human post-editing for as many segments as possible by accurately predicting if each machine-translated segment requires human post-editing or not. It also opens up other possibilities for controlling and increasing speed, quality, and scale. With the neural revolution, quality prediction has evolved from early academic research on “machine translation quality estimation” to API products integrated into translation management systems and adopted by global companies and institutions with high-volume post-editing workflows.
Takeaways: Attendees will learn about use cases, results, and adoption of translation quality prediction.