34.1337,

[The LINGUIST List]

LINGUIST List: Vol-34-1337. Thu Apr 27 2023. ISSN: 1069 - 4875.

Subject: 34.1337, 

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Editor for this issue: Lauren Perkins <lauren at linguistlist.org>
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Date: 23-Apr-2023
From: Lauren Perkins [lauren at linguistlist.org]
Subject: Rising Star: Anna Sophia Stein


During our annual Fund Drive, we like to feature undergraduate and MA
students who have gone above and beyond the classroom to participate
in the wider field of linguistics. Selected nominees exemplify a
commitment to not only academic performance, but also to the field of
linguistics and principles of scientific inquiry. Since this year’s
Fund Drive theme is Future tense, we are especially thankful to be
able to highlight undergraduate and MA students who are emerging as
the future leaders in our field.

Today’s Rising Star is Anna Sophia Stein, an undergraduate student at
Heinrich Heine University Düsseldorf. She was nominated by her
mentors, Prof. Dr. Kevin Tang and Akhilesh Kakolu Ramarao.

Anna is a final year BA student in a Linguistics programme in Germany,
currently completing her BA thesis on using psychologically-motivated
learning models (naive discriminative learning) to explain variation
in phonetic details. During her degree, she discovered an interest in
computational linguistics. She started working in the SLAM research
lab (https://slam.phil.hhu.de/) six months ago and has shown
tremendous progress in terms of both breadth and depth of her academic
development.

She is proactive, always curious, and has a can-do attitude. Her
ability to self learn was particularly impressive. In under 6 months,
her mentors witnessed a rapid growth in her computational linguistic
ability, from coding with Python, documenting in Git [1], to using a
High Performance Computing system. This was evidenced by her recent
victory in a competitive university-wide hackathon on Natural Language
Processing [2] – her team came 2nd (with another linguistic student
and a law student) [3].

Besides her personal development, she fosters learning for others in
multiple ways. First, she has tutored for multiple linguistic courses
both theoretical (pragmatics and Intro to linguistics) and
computational (quantitative methods for linguistic data, and
programming for linguists). Currently, she’s serving as a teaching
assistant for the course Language Technology for linguists with
Internet of Things (IoT) as a tech support by helping students in
setting up the devices [4]. Second, she actively contributes to free
and open source software and education materials, for instance, by
participating in Hacktoberfest 2022, co-organising the FOSS4ALL
workshop on open-source software for all disciplines [5], creating
tutorials on Github [6], and being a core contributor to my lab’s open
source projects. Finally, beyond research and teaching, she also
serves the linguistic community by being part of the organization team
of the upcoming 32nd TaCoS student conference for computational
linguists in Germany.

Anna Sophia writes:

Language models like ChatGPT have been a much-debated topic recently,
generally, but also in Linguistics. Consequences for both teaching and
research have amply been discussed with varying degrees of opinions.
It is known that even though ChatGPT can be a helpful tool for writing
and research, it hallucinates citations, facts, and other information.

I feel that it is particularly important to offer students and
researchers the opportunity to understand the inner workings of these
models so that they can benefit from these tools yet are aware of
their disadvantages and whether or not to use them. At a fundamental
level, this requires stirring interest in people, explaining how these
technologies work, and beginning to question them. It has been my
experience that people in the humanities tend to be more cautious/shy
about topics like programming and technology, especially because some
people chose a study program in the humanities because they wanted to
avoid math or technology.

This was also true for me when I first started to study Linguistics.
Over time, however, I became more interested in computational topics
and began to learn about them. This was greatly facilitated by people
in my environment who helped me on my journey, and I aspire to help
others do the same. This is why, as part of my work with the Speech
Lexicon and Modelling Lab at HHU, I have been developing tutorials and
other open-source resources for various topics, all aimed at people
with little to no prior knowledge. As a result, many resources and
documentations that I have written are a product of my own challenges,
such as acquiring proficiency with programming languages, setting up
programs and software, and using open-source tools. Others are a
result of my work as a teaching assistant for seminars in the area of
digital humanities and programming, specifically aimed at linguists
and people from the humanities. The skills acquired in these classes
and materials are helpful for any student or researcher, regardless of
whether they want to pursue a job in academia, the industry, or other
areas.

Another obstacle I see for students that are already interested in
learning about computational and technological topics is the pressure
of delivering results in an academic setting. It can be daunting to
start something without prior knowledge, especially knowing that this
will most likely impact future grades and potentially face negative
judgment from teaching staff and professors. As part of the organizing
committee for the TaCoS, a computer linguistics conference for
students only, the team and I make active efforts to reach out to as
many students as possible and facilitate their interest in
computational linguistics, regardless of their prior knowledge. Since
no professors/teaching staff are attending, students can present their
work and get feedback and inspiration from other students in the field
without feeling pressured to perform well or worrying that they are
asking the wrong questions. This will hopefully encourage students to
pursue their interests outside of the conference.

Looking ahead, it is also interesting to consider the importance of
open-source technology in linguistics. Specific tools and software,
for example, neural networks, have found great resonance in some
research communities, partially because of their wider availability.
Other applications, which are slightly more specific to certain
research areas, have yet to be made public and are only passed around
among fellow researchers. This makes it considerably more challenging
to get into this line of research without knowing the right people and
creates an air of elitism around these tools and their related
research. In addition, proprietary or private code and software
contribute to the looming replication crisis for Linguistics. That is
why Akhilesh Kakolu Ramarao and I held a workshop on free and
open-source software for research and have been advocating for it
during talks in classes. We hope this will enable students and
researchers to lower the barrier of entry, understanding, and
reproducibility of research.

This topic further relates to the general role of language models and
technology in linguistic research. For example, language models such
as neural networks have been used for a while in linguistic analysis
in order to inform linguistic theory or investigate certain phenomena.
However, there has always been a conversation about how well these
models can represent language structures in our brains and how
applicable their results are to humans if they are unrepresentative,
even if they can model prominent linguistic phenomena. My own interest
in this topic has developed throughout my Bachelor's degree and has
led me to write my Bachelor's thesis on using
psychologically-motivated learning models (Naive Discriminative
Learning) to explain variation in phonetic details. Other projects for
my work at the Slam Lab also pursue this topic and aim to compare the
results of psychologically motivated models with models that are not.

I hope that in the future, my research can contribute to developing
more psychologically motivated language models, and I can continue my
passion for bringing digital skills to humanity students.

[1] https://github.com/ansost
[2] https://www.heicad.hhu.de/en/aktivitaeten/translate-to-english-hhu
-legal-hackathon-2022
[3]
https://twitter.com/BraveMoneyLute/status/1583411783993544706/photo/1
[4] https://tinyurl.com/2jnt25yy
[5] https://slam.phil.hhu.de/libreslam
[6] https://docs.slam.phil.hhu.de/

_______________________

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