35.2135, Calls: Natural language and AI. New perspectives for linguistic studies (@ÖLT48)

[The LINGUIST List]

LINGUIST List: Vol-35-2135. Mon Jul 29 2024. ISSN: 1069 - 4875.

Subject: 35.2135, Calls: Natural language and AI. New perspectives for linguistic studies (@ÖLT48)

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Date: 27-Jul-2024
From: Nicholas Catasso [catasso at uni-wuppertal.de]
Subject: Natural language and AI. New perspectives for linguistic studies (@ÖLT48)


Full Title: Natural language and AI. New perspectives for linguistic
studies (@ÖLT48)
Short Title: ÖLT48

Date: 17-Dec-2024 - 19-Dec-2024
Location: Innsbruck, Austria
Contact Person: Nicholas Catasso
Meeting Email: catasso at uni-wuppertal.de
Web Site: https://www.uibk.ac.at/de/sprachwissenschaft/news/olt2024/

Linguistic Field(s): Computational Linguistics; Morphology;
Sociolinguistics; Syntax; Translation

Call Deadline: 01-Sep-2024

Meeting Description:

In recent years, Artificial Intelligence (AI) has made significant
strides across various disciplines. The integration of AI
applications, particularly Large Language Models (LLM), into
linguistic studies has opened new horizons for the analysis of natural
language. From morphosyntax to semantics and variation linguistics,
these technologies provide linguists with the opportunity to explore
complex linguistic phenomena. This development has led to the
automation of linguistic tasks such as text generation, translation,
and corpus annotation to an extent that was previously unimaginable.

However, the application of AI in linguistic research also reveals
challenges. One significant issue lies in the need to provide adequate
training data for AI models, covering a wide range of linguistic
phenomena and structures. Often, these data are incomplete, uneven, or
even erroneous, which can compromise the reliability of AI systems.
Another obstacle is the fact that AI models may inherit implicit
biases from the existing data on which they are trained. This can
result in distortions in the results and compromise the neutrality and
objectivity of linguistic analyses. Furthermore, language variation
seems to pose a challenge. AI models must be able to recognize and
process this diversity appropriately. This is often difficult as the
models may be constrained by certain linguistic patterns or norms.

The planned workshop will be a forum to discuss how and to what extent
AI applications (such as ChatGPT, DialoGPT, Meena, BlenderBot, etc.)
may be relevant for linguistic studies. By merging theoretical
approaches in linguistics with modern AI methods, the potentials and
challenges of these technologies for linguistic research will be
explored.

The workshop will focus on ‒ but will not be limited to ‒ the
following research questions:

 - How can AI applications be used to investigate and compare
grammaticality in different languages? Which applications come closest
to native speaker intuition?
 - To what extent can Large Language Models (LLM) be used for
automatic corpus annotation and analysis to identify and understand
linguistic variation?
 - What role do semantic models and neural networks play in
translating between languages with different syntactic structures?
 - How can Natural Language Processing (NLP) techniques be used to
examine the meaning and usage of language variation in different
social contexts?

Call for Papers:

Linguists from various theoretical orientations are invited to
participate in this working group and present empirical studies
addressing current developments and future perspectives in the field
of natural language processing and AI for linguistic studies. The
language of the workshop is English.

Abstracts for 20-minute presentations (plus 10 minutes discussion)
must be submitted in .doc and in PDF format. They should not exceed
one page in length (excluding references), in a font size no less than
12pt, and with margins of 1 inch/2.5cm. Every author can submit at
most one single-authored abstract and one co-authored abstract.

Abstracts are to be submitted via e-mail no later than September 01st,
2024 to:
catasso at uni-wuppertal.de
thomas.scharinger at uni-jena.de

Contact persons:
Nicholas Catasso (catasso at uni-wuppertal.de)
Thomas Scharinger (thomas.scharinger at uni-jena.de)



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