How long are you here?
I started as a working student at blc in October 2020 during my bachelor’s degree in computer science and continue this activity during my current master’s degree, so that I have been working here for a total of about 3 years.
What are you doing? What is important here?
In my day-to-day work, I mainly focus on the automated analysis and cleansing of large amounts of natural language data. These can be terminology databases or translation memories in XML formats, but I also work with other data formats.
It is crucial that I process and evaluate data from different sources and formats. One of the key challenges is to maintain an overview and plan a well structured approach in order to gradually work towards the respective goal.
What challenges and opportunities are there in your job?
When dealing with voice data and voice technologies, complicated problems often arise that do not have a predetermined solution. I always find these challenges fascinating, as they give me the opportunity to develop creative solutions and methods.
As technology in this area is developing rapidly, it is also important that the the computational linguistics team, including me, keep up to date with the latest developments. Apart from keeping ourselves informed we regularly inform the rest of the team about exciting new technologies.
Why do you like working at blc so much?
Due to the constantly changing customer requirements in every project, there is hardly any risk of getting bored or getting stuck in your own expertise. This versatility is an aspect of my work that I particularly appreciate, as it gives me the opportunity to develop myself both personally and professionally continuously.
What's next?
On 26 October at 11 am, you can join my colleague Christian Eisold and Adam Bittlingmayer in their webinar ” AI for more efficient post-editing and review – Practical introduction to Machine Translation Quality Prediction with ModelFront” Learn how training and the application of AI models can accelerate the post-editing process by predicting translation quality at the segment level.
To register , follow here.
