This report presents the results of the shared tasks organized as part of the VarDial Evaluation Campaign 2023. The campaign is part of the tenth workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with EACL 2023. Three separate shared tasks were included this year: Slot and intent detection for low-resource language varieties (SID4LR), Discriminating Between Similar Languages -- True Labels (DSL-TL), and Discriminating Between Similar Languages -- Speech (DSL-S). All three tasks were organized for the first time this year.

Automatic language identification is a challenging problem. Discriminating between closely related languages is especially difficult. This paper presents a machine learning approach for automatic language identification for the Nordic languages, which often suffer miscategorisation by existing state-of-the-art tools. Concretely we will focus on discrimination between six Nordic languages: Danish, Swedish, Norwegian (Nynorsk), Norwegian (Bokm{\aa}l), Faroese and Icelandic.

Articles that might be of interest to influencers. Inc.com--Google's spent the last 10 years studying the habits of effective managers. You can learn a lot from their conclusions. October 21, 2018 Go to Inc.com Small Biz Daily--To people outside the digital world, the term "hack" implies either a security breach or sleight of hand. You can't do as much as you think you can.

Machine learning, one of the core techniques in the field of artificial intelligence, involves teaching automated systems to devise new ways of doing things, by feeding them reams of data about the subject at hand. One of the big fears here is that biases in that data will simply be reinforced in the AI systems--and Amazon seems to have just provided an excellent example of that phenomenon. According to a new Reuters report, Amazon spent years working on a system for automating the recruitment process. The idea was for this AI-powered system to be able to look at a collection of resumes and name the top candidates. To achieve this, Amazon fed the system a decade's worth of resumes from people applying for jobs at Amazon.

Thanks to Amazon, the world has a nifty new cautionary tale about the perils of teaching computers to make human decisions. According to a Reuters report published Wednesday, the tech giant decided last year to abandon an "experimental hiring tool" that used artificial intelligence to rate job candidates, in part because it discriminated against women. Recruiters reportedly looked at the recommendations the program spat out while searching for talent, "but never relied solely on those rankings." The misadventure began in 2014, when a group of Amazon engineers in Scotland set out to mechanize the company's head-hunting process, by creating a program that would scour the Internet for worthwhile job candidates (and presumably save Amazon's HR staff some soul crushing hours clicking around LinkedIn). "Everyone wanted this holy grail," a source told Reuters.