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UW Master of Science in Computational Linguistics (CLMS)
Faculty director Emily M. Bender and alumni Haley Lepp, Chen-Xi and Preeti Mohan share how the UW Master of Science in Computational Linguistics offers students a baseline understanding of the many ways computational linguistics is being used in the tech space today and the societal implications or ethics of building language technology. To learn more, visit: http://ms.spr.ly/63323QzIbb
Video Transcript:
[Emily M. Bender]
Language technologies are pervasive at this point because language is pervasive in how we live our lives. And there's a lot of need right now for people who understand exactly what those systems are doing and how to apply them sensibly and correctly, to be joining the workforce and we hope to be training those folks.
[Haley Lepp]
The field is changing and evolving really quickly. And so one of the things that you really learn in this program is how to learn, because in many ways, the content that you might be learning, if it's too specific, might be out of date within a few years or a few months.
[Chen-Xi]
The program gives you the foundational knowledge for you to learn further and for you to be able to learn quickly on the job.
[Preeti Mohan]
Whether you come from a computer science perspective or a social science perspective, or even from a completely different field, you can really get that foundation regardless of where you're coming from and if you're interested in the things the program has to teach you, the program will support you through learning that.
[Emily M. Bender]
We are deliberate about creating cohorts who bring diverse expertise and diverse lived experience so that they can learn from each other.
[Preeti Mohan]
If someone has a different perspective than you, they're going to ask different questions that you might have never thought of. There are so many different domains and so many specializations that people have. And, you know, there's different ways of thinking in all these fields. So I think it really contributed to like the discussions and creativity that you had, especially during group projects or, you know, the questions people came and asked.
[Emily M. Bender]
Our teaching is called High-flex. Sometimes you hear about hybrid learning, where some of it's in-person and some of it's online. High-flex means that it's always available in both modalities. We have some students in the classroom, some students online. Everyone's taking the same class with the same deadlines. They work together in groups. Sometimes those groups are hybrid. If they're an online student from elsewhere, but they're in Seattle for a week, they can come in-person, and the hybrid format makes it super flexible.
[Haley Lepp]
I knew that courses had sort of flexible schedules and could be taken in many different ways. So if I took a quarter off or something, I could still continue with the with the degree.
[Chen-Xi]
You can do it really fast. I know people of my cohort who finished in one year. Like, very intensive, one year, in-person program and that's probably the best way things are being done. But if you have another job just like me, in the second year, I did part-time and I kind of paced myself, and it is also a good way to go.
[Emily M. Bender]
One of the things that we have developed in more recent years, starting in about 2016, is a strong focus on the societal implications or ethics of building language technology. And one of the great things about studying at the University of Washington is that you have a chance to work with people who've been thinking about this deeply for years now, both in our department and across campus.
[Haley Lepp]
This is a space to have radical conversations about computing and society. I think natural language processing is having a larger and larger effect on the world in its applications.
[Preeti Mohan]
The things you have to think about when you're designing models and, you know, curating data sets and, you know, the kind of ethical, moral and like, you know, social considerations you have to take. And for me personally, I thought that was like incredibly important.
[Emily M. Bender]
CLMS graduates go on to a variety of roles. Sometimes they go and they join large NLP groups at established companies. Sometimes they go on to Ph.D. programs. Sometimes they join companies that know they need language technology but don't actually have anyone in that area yet. And sometimes they go on to study actually very different things like audiology or education and the role of language technology in education. So we've graduated over 300 people over the years in this program, and every single one of those people knows what the training of CLMS means. And so they're out there working in the industry. Many are hiring managers now and eager to take additional people who've come through the program and also very willing to talk to people who are in the program now and network with them and give them advice on how to apply and where to apply.
