34.1195, Calls: Procedural and Computational Models of Semantic and Pragmatic Processes

Thu Apr 13 01:05:02 UTC 2023

LINGUIST List: Vol-34-1195. Thu Apr 13 2023. ISSN: 1069 - 4875.

Subject: 34.1195, Calls: Procedural and Computational Models of Semantic and Pragmatic Processes

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Date: 13-Apr-2023
From: Fabian Schlotterbeck [fabian.schlotterbeck at uni-tuebingen.de]
Subject: Procedural and Computational Models of Semantic and Pragmatic Processes

Full Title: Procedural and Computational Models of Semantic and
Pragmatic Processes
Short Title: Pros&Comps

Date: 31-Jul-2023 - 04-Aug-2023
Location: Ljubljana, Slovenia
Contact Person: Sonia Ramotowska
Meeting Email: prosandcomps at gmail.com
Web Site: https://prosandcomps.github.io

Linguistic Field(s): Cognitive Science; Computational Linguistics;
Pragmatics; Psycholinguistics; Semantics

Call Deadline: 05-May-2023

Meeting Description:

Procedural and computational modeling frameworks have been applied
successfully to various aspects of semantic and pragmatic processes,
yielding not only a good fit to empirical data but also insights of
theoretical relevance. On the one hand, computational (e.g., Bayesian
or information theoretic) models rationalize speaker behavior and
explain how a listener can use given information efficiently to infer
the intended meaning from an utterance. However, these models often
leave the stepwise processing of linguistic information unspecified.
On the other hand, procedural (e.g., automata or ACT-R) models explain
step-by-step cognitive processes behind meaning-related computations,
e.g., the process of building sentence representations. However, they
often lack the means to combine different information types in an
interactive fashion. The goal of this workshop is to bring together
researchers applying these two modeling methodologies to discuss their
strengths and weaknesses and work towards an integrated approach.

Call for Papers:

Computational methods in semantics and pragmatics have recently gained
in merit and popularity. One of the reasons for increased interest in
modeling is its usefulness in operationalizing abstract predictions of
formal semantics and pragmatics theories and linking them with
experimental data, yielding not only a good fit to empirical data but
also insights of theoretical relevance. Different modeling frameworks
have been proposed to explain various aspects of linguistic data. On
the one hand, some computational models propose domain-general,
unified computational-level (in the sense of Marr, 1982)
characterizations of meaning-related processes in order to rationalize
how speakers and listeners utilize language in communication. On the
other hand, procedural models describe language processing as the
execution of series of steps that speakers and listeners carry out
during language processing to compute utterance meanings. Both of
these approaches have specific strengths and weaknesses but they also
have the potential to complement each other.

In the field of experimental pragmatics, information theoretical and
Bayesian models received much attention as they excel in capturing the
dynamic interactions between speakers and listeners. Iterated response
models (such as RSA, Frank & Goodman, 2012; or Franke, 2009), in
particular, are able to explain linguistic phenomena at the
semantics-pragmatics interface (e.g., scalar implicature computation),
or effects of discourse and sociolinguistic factors (e.g., Questions
Under Discussion or politeness; see Scontras, Tessler, & Franke, 2018,
for review). These models provide an abstract explanation of how
humans compute meaning but leave unspecified how this computation
unfolds over time.

Procedural models, by contrast, zoom in on the algorithms (in the
sense of Marr’s, 1982, second level) underlying meaning computation
and propose sequences of processing steps, potentially executed via
different modules. For example, in the domain of experimental
semantics, procedural models (e.g. Szymanik, 2016; Bott, Schlotterbeck
& Klein, 2019) were applied to quantifier interpretation and cognitive
architectures such as ACT-R (Anderson, 2007) can capture a broad range
of complex linguistic processes (Brasoveanu & Dotlacil, 2019).

The goal of this workshop is to bring together researchers applying
different modeling methodologies. We invite submissions that present
state-of-the-art applications of computational and procedural models
or discuss strengths and limitations of each of the mentioned
methodologies. Moreover, because we see great potential for integrated
computational and procedural models, we strongly encourage submissions
that propose hybrid approaches. Such hybrid approaches may, for
example, include sequential sampling decision models (e.g.
Schlotterbeck et al., 2020; Ramotowska et al., 2023) or models of
incremental interpretation (e.g. Cohn-Gorden et al., 2019; Waldon &
Degen, 2021) as procedural extensions of Bayesian approaches.

Topics of interest include (but are not limited to):

-procedural and/or computational models of incremental interpretation,
-procedural and/or computational models of language comprehension and
production,
-procedural and/or computational models of verification or inferences,
-behavioral and neurocognitive procedural and/or computational models,
-procedural and/or computational models of the semantics-pragmatics
interface,
-procedural and/or computational models of context effects (e.g.,
politeness, conversation goals, informativeness) on interpretation,
and
-procedural and/or computational models of interaction between
language-specific and domain-general interpretation mechanisms.

Submissions guidelines: Abstracts should be anonymous and not exceed 2
pages (plus one extra page for figures, tables, glosses, references,
etc.) with 11 pt font size. Submissions can be made at:
https://easychair.org/conferences/?conf=proscomps2023

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