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.
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if it's too specific, might be out of date within a few years or a few months.
The program gives you the foundational knowledge for you to learn further
and for you to be able to learn quickly on the job.
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.
We are deliberate about creating cohorts who bring diverse
expertise and diverse lived experience so that they can learn from each other.
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.
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.
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.
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.
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.
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.
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.
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.
This department has such a diverse
area of expertise that
you can really get a hand on many different spaces.
And as a result, I think I got a little exposure to all those different spaces
and it helped me decide what was most interesting to me
and also have a baseline understanding of the many ways
that Computational Linguistics is being used in the tech space today.