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Today's Topics:
1. Second CfP: 19th Workshop on Multiword Expressions (MWE 2023)
@EACL2023 (Aaron Poet)
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Message: 1
Date: Tue, 17 Jan 2023 15:29:22 +0000
From: Aaron Poet <hanlifengaaron@gmail.com>
To: moses-support <moses-support@mit.edu>
Cc: Lifeng Han <lifeng.han@manchester.ac.uk>
Subject: [Moses-support] Second CfP: 19th Workshop on Multiword
Expressions (MWE 2023) @EACL2023
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Second Call for papers
Subject: Second CfP: 19th Workshop on Multiword Expressions (MWE 2023)
@EACL2023
*[Apologies for cross-postings]*
********************************************************************************
Second Call for Papers
19th Workshop on Multiword Expressions (MWE 2023)
Organized and sponsored by SIGLEX, the Special Interest Group
on the Lexicon of the ACL
Full-day workshop collocated with EACL 2023, Dubrovnik, Croatia, May 2 or
6, 2023
Hybrid (on-site & on-line)
Submission deadline: February 13, 2023
NEW: ARR commitment deadline: March 6, 2023
NEW: Special track on MWEs in Clinical NLP (see below)
NEW: Best paper award (see below)
MWE 2023 website: <https://multiword.org/mwe2022/>
https://multiword.org/mwe2023/
********************************************************************************
Multiword expressions (MWEs) are word combinations that exhibit lexical,
syntactic, semantic, pragmatic, and/or statistical idiosyncrasies (Baldwin
& Kim 2010), such as by and large, hot dog, pay a visit and pull one's leg.
The notion encompasses closely related phenomena: idioms, compounds,
light-verb constructions, phrasal verbs, rhetorical figures, collocations,
institutionalised phrases, etc. Their behaviour is often unpredictable; for
example, their meaning often does not result from the direct combination of
the meanings of their parts. Given their irregular nature, MWEs often pose
complex problems in linguistic modelling (e.g. annotation), NLP tasks (e.g.
parsing), and end-user applications (e.g. natural language understanding
and MT), hence still representing an open issue for computational
linguistics (Constant et al. 2017).
For almost two decades, modelling and processing MWEs for NLP has been the
topic of the MWE workshop organised by the MWE section
<https://multiword.org/> of SIGLEX <http://www.siglex.org/> in conjunction
with major NLP conferences since 2003. Impressive progress has been made in
the field, but our understanding of MWEs still requires much research
considering their need and usefulness in NLP applications. This is also
relevant to domain-specific NLP pipelines that need to tackle terminologies
most often realised as MWEs. Following previous years, for this 19th
edition of the workshop, we identified the following topics on which
contributions are particularly encouraged:
-
MWE processing and identification in specialized languages and domains:
Multiword terminology extraction from domain-specific corpora (Bonin et al.
2010) is of particular importance to various applications, such as MT
(Semmar & Laib, 2017), or for the identification and monitoring of
neologisms and technical jargon (Chatzitheodorou et al, 2021). We expect
approaches that deal with the processing of MWEs as well as the processing
of terminology in specialised domains can benefit from each other.
-
MWE processing to enhance end-user applications: MWEs have gained
particular attention in end-user applications, including MT (Zaninello &
Birch 2020; Han et al. 2021
<https://aclanthology.org/2021.nodalida-main.35/>, 2022
<https://doras.dcu.ie/26559/>), simplification (Kochmar et al. 2020),
language learning and assessment (Paquot et al. 2019; Christiansen & Arnon
2017), social media mining (Maisto et al. 2017), and abusive language
detection (Zampieri et al. 2020; Caselli et al. 2020). We believe that it
is crucial to extend and deepen these first attempts to integrate and
evaluate MWE technology in these and further end-user applications.
-
MWE identification and interpretation in pre-trained language models: Most
current MWE processing is limited to their identification and detection
using pre-trained language models, but we still lack understanding about
how MWEs are represented and dealt with therein (Nedumpozhimana & Kelleher
2021; Garcia et al. 2021, Fakharian & Cook 2021), how to better model the
compositionality of MWEs from semantics (Moreau et al. 2018
<http://www.tara.tcd.ie/bitstream/handle/2262/91208/204-3-1302-1-10-20181025.pdf?sequence=1>).
Now that NLP has shifted towards end-to-end neural models like BERT,
capable of solving complex tasks with little or no intermediary linguistic
symbols, questions arise about the extent to which MWEs should be
implicitly or explicitly modelled (Shwartz & Dagan, 2019).
-
MWE processing in low-resource languages: The PARSEME shared tasks
(Ramisch et al. 2020; 2018; Savary et al. 2017), among others, have
fostered significant progress in MWE identification, providing datasets
that include low-resource languages, evaluation measures, and tools that
now allow fully integrating MWE identification into end-user applications.
A few efforts have recently explored methods for the automatic
interpretation of MWEs (Bhatia, et al. 2018; 2017), and their processing in
low-resource languages (Liu & Wang 2020; Kumar et al. 2017). Resource
creation and sharing should be pursued in parallel with the development of
methods able to capitalize on small datasets (Han et al. 2020
<https://aclanthology.org/2020.mwe-1.6/>).
Through this workshop, we would like to bring together and encourage
researchers in various NLP subfields to submit MWE-related research, so
that approaches that deal with processing of MWEs including processing for
low-resource languages and for various applications can benefit from each
other. We also intend to consolidate the converging effects of previous
joint workshops LAW-MWE-CxG 2018
<http://multiword.sourceforge.net/lawmwecxg2018/>, MWE-WN 2019
<http://multiword.sourceforge.net/mwewn2019/> and MWE-LEX 2020
<http://multiword.sourceforge.net/mwelex2020/>, the joint MWE-WOAH panel in
2021 <https://multiword.org/mwe2021/#program>, and the MWE-SIGUL 2022 joint
session <https://multiword.org/mwe2022/>, extending our scope to MWEs in
e-lexicons and WordNets, MWE annotation, as well as grammatical
constructions. Correspondingly, we call for papers on research related (but
not limited) to MWEs and constructions in:
-
Computationally-applicable theoretical work in psycholinguistics and
corpus linguistics;
-
Annotation (expert, crowdsourcing, automatic) and representation in
resources such as corpora, treebanks, e-lexicons, and WordNets (also for
low-resource languages);
-
Processing in syntactic and semantic frameworks (e.g. CCG, CxG, HPSG,
LFG, TAG, UD, etc.);
-
Discovery and identification methods, including for specialized
languages and domains such as clinical or biomedical NLP;
-
Interpretation of MWEs and understanding of text containing them;
-
Language acquisition, language learning, and non-standard language (e.g.
tweets, speech);
-
Evaluation of annotation and processing techniques;
-
Retrospective comparative analyses from the PARSEME shared tasks;
-
Processing for end-user applications (e.g. MT, NLU, summarisation,
language learning, etc.);
-
Implicit and explicit representation in pre-trained language models and
end-user applications;
-
Evaluation and probing of pre-trained language models;
-
Resources and tools (e.g. lexicons, identifiers) and their integration
into end-user applications;
-
Multiword terminology extraction;
-
Adaptation and transfer of annotations and related resources to new
languages and domains including low-resource ones.
Shared Task
We do not have a shared task this year, but a new release of the PARSEME
corpus of verbal MWEs is currently underway. We encourage submission of
research papers that include analyses of the new edition of the PARSEME
data and improvements over the results for PARSEME 2020 shared task as well
as SemEval 2022 task 2 on idiomaticity prediction.
*** Special Track on MWEs in Clinical NLP ***
Pursuing the MWE Section?s tradition of synergies with other communities,
this year, we are organizing a joint session with the Clinical NLP workshop
<https://clinical-nlp.github.io/2022/> for shared papers/poster
presentations. Since clinical texts contain an important amount of
multiword expressions (e.g. medical terms or domain-specific collocations),
a joint session is deemed beneficial for both communities. The goal is to
foster future synergies that could address scientific challenges in the
creation of resources, models and applications to deal with multiword
expressions and related phenomena in the specialised domain of ClinicalNLP.
Submissions describing research on MWEs in the specialized domain of
ClinicalNLP, especially introducing new datasets or new tools and
resources, are welcome. Papers accepted in this track will have the option
to present their work in the Clinical NLP workshop at ACL 2023 as well,
after being presented at MWE 2023.
Best paper award
All full papers in the workshop will be considered by the program committee
for the best paper award.
Submission formats
The workshop invites two types of submissions:
-
archival submissions that present substantially original research in
both long paper format (8 pages + references) and short paper format (4
pages + references).
-
non-archival submissions of abstracts describing relevant research
presented/published elsewhere which will not be included in the MWE
proceedings.
Paper submission and templates
Papers should be submitted via the workshop's START submission page (link
will be provided once available). Please choose the appropriate submission
format (archival/non-archival). Archival papers with existing reviews will
also be accepted through the ACL Rolling Review. Submissions must follow
the ACL 2023 stylesheet
<https://2023.aclweb.org/calls/style_and_formatting/>.
Archival papers with existing reviews from ACL Rolling Review will also be
considered. A paper may not be simultaneously under review through ARR and
MWE. A paper that has or will receive reviews through ARR may not be
submitted for review to MWE.
Important Dates
Paper submission: February 13, 2023
ARR paper commitment: March 6, 2023
Notification of acceptance: March 13, 2023
Camera-ready papers due: March 27, 2023
Workshop: May 2 or 6, 2023
All deadlines are at 23:59 UTC-12 (Anywhere on Earth).
Organizing Committee
Program chairs: Marcos Garcia, Voula Giouli, Lifeng Han
<https://research.manchester.ac.uk/en/persons/lifeng.han>, Shiva Taslimipoor
Publication chair: Archna Bhatia
Publicity chair: Kilian Evang
Anti-harassment policy
The workshop follows the ACL anti-harassment policy
<https://www.aclweb.org/adminwiki/index.php?title=Anti-Harassment_Policy>.
Contact
For any inquiries regarding the workshop, please send an email to the
Organizing Committee at mweworkshop2023@googlegroups.com.
--
*News*: ClinicalMT
<https://scholar.google.com/citations?view_op=view_citation&hl=en&user=_vf3E2QAAAAJ&sortby=pubdate&citation_for_view=_vf3E2QAAAAJ:4fGpz3EwCPoC>@WMT22
| Meta-eval Tutorial
<https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=5418617987092956707>
/
HumanEval <https://aclanthology.org/2022.lrec-1.2.pdf> (paper_w_tool) /
TranslationUncertainty <https://aclanthology.org/2022.lrec-1.2.pdf> (paper)
@LREC22 | ClinicalTextMinging
<https://github.com/poethan/TransformerCRF> (Tools)
@HealTAC2022 *|* Covid-Topic-Modeling <https://arxiv.org/abs/2301.03029> (
*arXiv-2023*)
*CFPs*: HealTAC23 <http://healtex.org/healtac-conference-series/>
(Manchester June) | MWE23 <https://multiword.org/mwe2023/>@EACL (joint w
ClinicalNLP)
Ph.D. in Computer Application (Machine Translation, thesis
<https://doras.dcu.ie/26559/>), M.Sc. (Software Engineering, thesis
<https://arxiv.org/abs/1703.08748> *excellent-award*), B.Sc. (Math, *GPA
80/100*)
Google-Scholar <https://scholar.google.nl/citations?user=_vf3E2QAAAAJ&hl=en> ,
Presentation <https://www.slideshare.net/AaronHanLiFeng>(ppt),
Research-Gate <https://www.researchgate.net/profile/Aaron_L-F_Han>
Google-site <https://sites.google.com/view/poetgarden/home> Linkedin
<https://www.linkedin.com/in/aaronhan/>, Writer
<https://books.apple.com/us/author/lifeng-han/id1602229739>(poetry)
Postdoctoral <https://www.research.manchester.ac.uk/portal/lifeng.han.html>
Research Associate at HECTA
<https://www.research.manchester.ac.uk/portal/en/researchers/goran-nenadic(4563fc16-68b4-46f5-a28e-5c67a2cab488)/mygroup.html>
group, The University of Manchester, UK
https://www.research.manchester.ac.uk/portal/lifeng.han.html Former: ADAPT
Research Centre & DCU, Ireland
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