LTI

Time: 
Wednesday, September 14, 2022 - 10:30 to 12:00

Talks

Language Technology Latest Trends in 2022

Nimdzi collects data from providers of more than 700 technology solutions every year. We will present the results of our 2022 Language Technology Atlas and share the most exciting news with the audience. The data gathering behind the Atlas is based on four primary sources: survey replies, briefings and meetings with language technology firms, publicly available data, and personal experience of Nimdzi team members who regularly use dozens of language tools. The audience will walk away with insights on: 1. Major changes and developments 2. Specific trends and challenges 3.

The Translation Impact of the Focus on Global CX

As global enterprises focus on improving CX (customer experience) across the world we see the following impact: - Huge increase in dynamic, unstructured CX related content - Substantial increase in content and translation volumes - Increased use of "raw" MT - Changes in the view of translation quality - Changes in the kinds of tools and processes used to enable effective massive-scale translation capabilities. This presentation will provide examples of the content changes and their impact on optimal tools and the translation production process.

The Need for Anonymization for Effective Data Mining

We produce data at an ever-increasing rate yet turning that data into valuable information and insights is increasingly challenging due to the data privacy requirements involved. In this presentation we explore what is currently possible based on real-life examples and discuss anonymization challenges and approaches.

New Perspectives in Automatic Translation Analysis

For many years translation software has been making extensive use of natural language processing techniques. These techniques are used to develop features which help translators carry out their work. In this presentation we will describe a novel approach to post-translation analysis: MFTA (Multi-faceted Translation Analysis). It relies on a combination of natural language processing techniques such as stemming, lemmatization, pos-tagging, and parsing. It also involves the proprietary mechanism of computing word similarities between languages, called ILVS (Inter-language Vector Space).