34.1304, Rising Star: Angela Cao

[The LINGUIST List]

LINGUIST List: Vol-34-1304. Mon Apr 24 2023. ISSN: 1069 - 4875.

Subject: 34.1304, Rising Star: Angela Cao

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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: Angela Cao


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 Angela Cao, an MA student at University of
Edinburgh. She was nominated by her mentor, Dr. Lelia Glass.

Angela graduated from Emory University in 2023 with degrees in
Mathematics and Linguistics. At Emory, she worked as the Lead
Annotator in Jinho Choi's NLP group, first-authoring a paper about the
annotation of causation in text. Angela was chosen as an intern at the
highly selective Center for the Study of Language and Information at
Stanford (Summer 2021), working with Thomas Icard on causal models.

Angela took the bus from Emory to Georgia Tech to take a semantics
class, after which she was recruited by Lelia Glass to work on a
collaborative NSF grant held between Georgia Tech and the University
of Rochester (PI: Aaron Steven White). The grant uses ChatGPT to
generate standardized sentences containing verbs and content nouns,
such as "The manager ran the organization" and "The athlete ran the
race," which are used to elicit human annotations for the aspectual
properties of such verbs, in turn shedding light on the role of verbs
versus sentences in aspectual inferences.  Leading an effort to
experimentally validate this way of generating sentences, Angela has
wrangled with Qualtrics, quickly learned Python, and written several
sections of an in-progress manuscript.

Even as an undergraduate, Angela pursued teaching alongside her
research, serving as a TA for a Data Science course and a tutor for
both math and linguistics at Emory. Her advisors can attest that she
is a natural leader who can explain complicated ideas in a clear and
welcoming manner.

Angela is pursuing a master's degree in Linguistics at the University
of Edinburgh and plans to begin a Ph.D. program in the fall, where she
will further explore how humans understand events and how computers
can approximate those inferences, drawing on her skills in human
subjects research and computational modeling as well as her deep
understanding of language.

Angela writes:

At a high level, my goal is to illuminate the inferences that people
draw from sentences, using both the meanings of words and one's
broader knowledge of the world. I am particularly interested in the
interpretation of causal language and how people both draw inferences
about causal relations between events, as well as likely implicit
events that happen given the explicitly stated ones. Related to my
interests, the field of linguistics has several emerging threads that
I am especially excited about.

Firstly, there is a growing trend in the study of language that aims
to model meaning using frameworks from other areas, such as decision
or prototype theory. Building upon these existing notions not only
emphasizes the nature of linguistics as inherently interdisciplinary,
but enables linguistic findings to more easily find applications
outside of academia. For example, work on the semantics of causal
verbs can be used to design psychological studies that investigate how
participants can be primed intra-experiment to be more open to climate
change discourse, which in turn can influence how educational systems
approach teaching students about this topic. In line with this theme,
my Master’s thesis aims to evaluate the underlying conceptual scale of
the causal senses of “make” and “force” by comparing the predictions
of two possible models taken from the field of causality.

Secondly, the recent emergence of high-performing language models
makes me enthused by work that explores how their distributional
representations can inform us about our own conceptual spaces.
Although these representations are currently opaque to any attempts at
feature-based interpretation, I believe that linguists have a lot to
contribute to and gain from working with these representations.
Relatedly, I currently work with graduate student Stephanie Richter in
collecting data on how these models produce prototypical and/or
natural descriptions of events. More broadly, this project will
provide insight on how automatic, corpus and human-based methods
compare in their understanding of how verbs and their relata fit
together.

After finishing my Master’s degree, I am excited to pursue my Ph.D. at
the University of Rochester where I will continue to work on the
SuperMereo (i.e., super-mereology) project in Prof. Aaron Steven
White’s Formal and Computational Semantics Lab. I hope to contribute
to the growing subfield of research that aims to model causal event
structure in language, and the inferences that these structures
generate.

__________________

The LINGUIST List looks forward to continuing to serve the linguistics
community, including its up-and-coming stars, for years to come. To
that end, please consider a contribution to our Fund Drive:
https://funddrive.linguistlist.org/donate



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