33.1252, Calls: Computational Linguistics, Semantics/France

Fri Apr 8 05:59:45 UTC 2022

LINGUIST List: Vol-33-1252. Fri Apr 08 2022. ISSN: 1069 - 4875.

Subject: 33.1252, Calls: Computational Linguistics, Semantics/France

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Date: Fri, 08 Apr 2022 01:59:08
From: Begoña Altuna [begona.altuna at ehu.eus]
Subject: Wordnets in the Deep Learning Era 2022 Workshop

Full Title: Wordnets in the Deep Learning Era 2022 Workshop 
Short Title: WDLE 2022 

Date: 24-Jun-2022 - 24-Jun-2022
Location: Marseille, France 
Contact Person: Begoña Altuna
Meeting Email: begona.altuna at ehu.eus
Web Site: http://ixa2.si.ehu.eus/wdle2022/ 

Linguistic Field(s): Computational Linguistics; Semantics; Text/Corpus Linguistics 

Call Deadline: 11-Apr-2022 

Meeting Description:

In recent years, the NLP community is contributing to the emergence of
powerful new deep learning techniques and large multilingual pre-trained
language models that are revolutionizing the approach to most NLP tasks. Just
a short time ago, nobody could have predicted the recent breakthroughs that
have resulted in systems able to deal with unseen tasks (Wei et al. 2021; Sanh
et al. 2021; Min et al. 2021).

An NLP task that can largely contribute from this approach is building
large-scale lexical knowledge bases such as wordnets, as it is very time
consuming and requires large research groups and long periods of development
(Miller 1995; Fellbaum 1998; Gonzalez-Agirre et al. 2012; Bond and Paik 2012).

Lately, several new approaches have been devised towards its automatic
development. For instance, Watset (Ustanov et al. 2017) has been used for the
automatic induction of English and Russian synsets. Noraset et al. (2017) and
Gadetsky et al. (2018) propose different systems for automatically providing
definitions of words in their context. Sainz and Rigau (2020) infer without
training the domain label of a particular definition. Qi et al. (2020) propose
a reverse dictionary system that returns words semantically matching the input
definitions. Feng et al. (2021) addresses the concept-to-text generation task.
Barba et al. (2021) generate usage examples for a given set of words with
their definitions. Chen et al. (2021) automatically construct taxonomies from
pretrained language models.

On the other hand, as constructing benchmarks that test the abilities of
modern natural language understanding models is difficult, large-scale
knowledge bases are used to generate lexical semantic, world knowledge and
common sense probes (Ma et al 2021). For instance, Richardson and Sabharwal
(2020) use links in WordNet to generate question-answer pairs to evaluate
language models. Aspillaga et al. (2021) define a probing classifier based on
concept relatedness according to WordNet.

Additionally, it is worth investigating possible opportunities to leverage
both structured and unstructured information sources (Lauscher et al. 2020;
Colon-Hernandez et al. 2021; Lu et al. 2021). For instance, Peters et al.
(2019) enhance contextual representations with structured, human-curated
knowledge.

In this workshop we wish to look at how large language models can productively
interact with existing semantic networks. We also welcome approaches that use
language models for existing tasks, such as word sense disambiguation, or that
use semantic networks to augment language models.

Call for Papers:

Topics of Interest:
We invite submissions with original contributions addressing all topics
related to the productive interaction between large pre-trained language
models and large semantic networks. Areas of interest include, but are not
limited to, the following:

Building and enriching monolingual, multilingual and cross-lingual lexical
knowledge bases, semantic networks and wordnets using deep learning techniques
and large pre-trained language models.
Exploiting lexical knowledge bases, semantic networks and wordnets for
creating world knowledge and common sense probes for testing large pre-trained
language models.
Using lexical knowledge bases, semantic networks and wordnets for creating
prompts for zero-shot or few-shot or transfer learning NLP tasks.
Leveraging lexical knowledge bases, semantic networks and wordnets and large
pre-trained language models towards natural language understanding.
Submission & Publication
We accept research papers addressing WordNets and deep learning techniques.
Authors must declare if part of the paper contains material previously
published elsewhere.

We accept the following typologies of papers:
- Research papers.
- Research posters (work-in-progress, projects in early stage of development
or description of new resources or methods).

Papers should be written in English and all typologies are allowed a maximum
of 8 pages, references excluded. The program committee reserves the right to
decide whether a paper submitted as a research paper is better suited for a
poster presentation.

Accepted papers will be published in online proceedings.

Papers must strictly comply with the LREC stylesheet
(https://lrec2022.lrec-conf.org/en/submission2022/authors-kit/) and be
submitted in unprotected PDF format.

Submission page: https://www.softconf.com/lrec2022/Wordnets/

Each submission will be reviewed by three programme committee members. In
compliance with the LREC rules, papers must *not* be anonymized.

Important Dates:
Paper submission deadline: 11 April 2022
Notification of acceptance: 3 May 2022
Camera-ready paper: 23 May 2022
Workshop date: 24 June 2022

Invited Speakers
*TBA*

Organizing Committee
Javier Alvez (UPV/EHU)
Begoña Altuna (HiTZ, UPV/EHU)
Francis Bond (NTU)
Bolette Pedersen (U Copenhaguen)
Alexandre Rademaker (IBM Research and FGV/EMAP)
German Rigau (HiTZ, UPV/EHU)
Piek Vossen (VU)

To contact the organizers, please email Javier Alvez (javier.alvezehu.eus) or
Begoña Altuna (begona.altunaehu.eus) using Subject: [WDLE 2022].

Programme Committee (TBC)
Rodrigo Agerri (HiTZ, UPV/EHU)
Eneko Agirre (HiTZ, UPV/EHU)
Montse Cuadros (Vicomtech)
Filip Ilievski (ISI, USC)
Itziar Gonzalez-Dios (HiTZ, UPV/EHU)
Michael Goodman (LivePerson)
Egoitz Laparra (University of Arizona)
Luis Morgado da Costa (Palacky University Olomouc)
Maciej Piasecki (WUT)
Roberto Navigli (Sapienza University)
Didier Schwab (Grenoble)
Kiril Simov (BulTreeBank)
Aitor Soroa (HiTZ, UPV/EHU)
Pia Sommerauer (VU)

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