Joint Workshop on Multiword Expressions and Universal Dependencies (MWE-UD 2024)
Colocated with: LREC-COLING 2024 (Torino, Italia)
Date of the Workshop: May 25, 2024
Organised and sponsored by:
The Special Interest Group on the Lexicon (SIGLEX) of the Association for Computational Linguistics (ACL), SIGLEX’s Multiword Expressions Section (SIGLEX-MWE), Universal Dependencies (UD) and UniDive Cost Action CA21167.
News
- February 19, 2024: MWE-UD 2024 workshop final CfP posted with extended deadline (new submission date: March 3, 2024)
- February 19, 2024: MWE-UD 2024 workshop ARR commitment date set (ARR Commitment Date: March 25, 2024)
- January 31, 2024: Second CfP posted
- January 16, 2024: Keynote speaker Natalia Levshina confirmed
- January 18, 2024: Keynote speaker Harish Tayyar Madabushi confirmed
- December 8, 2023: First CfP posted
- December 8, 2023: MWE-UD 2024 workshop date confirmed (Workshop Date: May 25, 2024)
- November 21, 2023: MWE-UD 2024 proposal accepted to LREC-COLING 2024
- August 29, 2023: Organising committee formed
Contents on this page
Natalia Levshina: Using Universal Dependencies for testing hypotheses about communicative efficiency
Abstract: There is abundant evidence that language structure and use are
influenced by language users’ tendency to be efficient, trying to minimize the
cost-to-benefit ratio of communication (e.g., Hawkins, 2004; Gibson et al.,
2019; Levshina, 2022). In my talk I will show how data from corpora annotated
with Universal Dependencies can be used for testing hypotheses about the role
of communicative efficiency in shaping up language structure and use. The
hypotheses are as follows:
-
As discussed by typologists (Sapir, 1921; Sinnemäki, 2008), rigid word order can
compensate for lack of formal marking of core arguments. The hypothesis is then that
there are positive correlations between the entropy of subject and object in a transitive
clause in a corpus and the relative frequency of disambiguating case forms or verb
forms. These correlations are expected to minimize the articulation effort involved in
the use of argument flags or indices.
-
There is a positive correlation between semantic tightness (Hawkins, 1986),
operationalized as Mutual Information between lexemes and syntactic roles, and the
relative frequency of verb-final clauses in a corpus. Strong associations between
lexemes and roles help to avoid the costs of reanalysis in verb-final languages.
-
There is a negative correlation between the relative frequency of verb-final clauses in
the clause and the average number of overt core arguments, which helps to save
processing costs required for keeping longer dependencies in mind (cf. Ueno &
Polinsky, 2009).
These hypotheses will be tested on corpus data annotated with Universal Dependencies, with
the help of mixed-effects models with genealogical and geographic information as random
effects.
References
Gibson, Edward, Richard Futrell, Steven P. Piantadosi, Isabelle Dautriche, Kyle Mahowald,
Leon Bergen & Roger Levy. 2019. How efficiency shapes human language. *Trends in
Cognitive Science* 23(5): 389-407. https://doi.org/10.1016/j.tics.2019.02.003
Hawkins, John A. 1986. *A Comparative Typology of English and German: Unifying the
Contrasts*. London: Croom-Helm.
Hawkins, John A. 2004. *Efficiency and Complexity in Grammars*. Oxford: Oxford University
Press.
Levshina, Natalia. 2022. *Communicative Efficiency: Language Structure and Use*.
Cambridge: Cambridge University Press.
Sapir, Edward. 1921. *Language: An Introduction to the Study of Speech*. New York: Harcourt.
Sinnemäki, Kaius. 2008. Complexity trade-offs in core argument marking. In: Matti
Miestamo, Kaius Sinnemäki and Fred Karlsson (eds.), *Language Complexity: Typology,
Contact, Change*, 67–88. Amsterdam: John Benjamins.
Ueno, Mieko & Maria Polinsky. 2009. Does headedness affect processing? A new look at the
VO-OV contrast. *Journal of Linguistics* 45: 675–710.
Bio:
Abstract: Pre-Trained Language Models (PLMs), trained on the cloze-like
task of masked language modelling, have demonstrated access to a broad range of
linguistic information, including both syntax and semantics. Given their access
to both syntax and semantics, coupled with their data-driven foundations, which
align with usage-based theories, it is valuable and interesting to examine the
constructional information they encode. Early work confirmed that these models
have access to a substantial amount of constructional information. However,
more recent research focusing on the types of constructions PLMs can accurately
interpret, and those they find challenging, suggests that an increase in
schematicity correlates with a decline in model proficiency. Crucially,
schematicity—the extent to which constructional slots are fixed or allow for a
range of elements that satisfy a particular semantic role associated with the
slot—correlates to the extent of “reasoning” needed to interpret constructions,
a task that poses significant challenges for language models. In this talk, I
will begin by reviewing the constructional information encoded in both earlier
models and more recent large language models. I will explore how these aspects
are intertwined with the models’ reasoning abilities and introduce promising
new approaches that could integrate theoretical insights from linguistics with
practical, data-driven approaches of PLMs.
Bio: Dr. Tayyar
Madabushi’s
research focuses on understanding the fundamental mechanisms that underpin the
performance and functioning of Large Language Models. His work on LLMs was
included in the discussion paper on the Capabilities and Risks of Frontier AI,
which was used as one of the foundational research works for discussions at the
UK AI Safety Summit held at Bletchley Park. His research on the constructional
information encoded in language models has been influential in bringing
together the fields of construction grammar and pre-trained language models. In
addition, his work on language models includes collaborative industrial
research aimed at rectifying biases in speech-to-text systems widely utilised
across the UK. Before starting his PhD in automated question answering at the
University of Birmingham, Dr. Tayyar Madabushi founded and headed a social
media data analytics company based in Singapore.
Long Papers
MWE
Assessing BERT’s sensitivity to idiomaticity
Li Liu and Francois Lareau
Identification and Annotation of Body Part Multiword Expressions in Old Egyptian
Roberto Díaz Hernández
Lexicons Gain the Upper Hand in Arabic MWE Identification
Najet Hadj Mohamed, Agata Savary, Cherifa Ben Khelil, Jean-Yves Antoine, Iskandar keskes and Lamia Hadrich-Belguith
Revisiting VMWEs in Hindi: Annotating Layers of Predication
Kanishka Jain and Ashwini Vaidya
Towards the semantic annotation of SR-ELEXIS corpus: Insights into Multiword Expressions and Named Entities
Cvetana Krstev, Ranka Stanković, Aleksandra M. Marković and Teodora Sofija Mihajlov
To Leave No Stone Unturned: Annotating Verbal Idioms in the Parallel Meaning Bank
Rafael Ehren, Kilian Evang and Laura Kallmeyer
Sign of the Times: Evaluating the use of Large Language Models for Idiomaticity Detection
Dylan Phelps, Thomas M. R. Pickard, Maggie Mi, Edward Gow-Smith and Aline Villavicencio
BERT-based Idiom Identification using Language Translation and Word Cohesion
Arnav Yayavaram, Siddharth Yayavaram, Prajna Devi Upadhyay and Apurba Das
UD
Automatic Manipulation of Training Corpora to Make Parsers Accept Real-world Text
Hiroshi Kanayama, Ran Iwamoto, Masayasu Muraoka, Takuya Ohko and Kohtaroh Miyamoto
Universal Feature-based Morphological Trees
Federica Gamba, Abishek Stephen and Zdeněk Žabokrtský
Universal Dependencies for Saraiki
Meesum Alam, Francis Tyers, Emily Hanink and Sandra Kübler
Strategies for the Annotation of Pronominalised Locatives in Turkic Universal Dependency Treebanks
Jonathan Washington, Çağrı Çöltekin, Furkan Akkurt, Bermet Chontaeva, Soudabeh Eslami, Gulnura Jumalieva, Aida Kasieva, Aslı Kuzgun, Büşra Marşan and Chihiro Taguchi
MWE+UD
Fitting Fixed Expressions into the UD Mould: Swedish as a Use Case
Lars Ahrenberg
Part-of-Speech Tagging for Northern Kurdish
Peshmerge Morad, Sina Ahmadi and Lorenzo Gatti
Combining Grammatical and Relational Approaches. A Hybrid Method for the Identification of Candidate Collocations from Corpora
Damiano Perri, Irene Fioravanti, Osvaldo Gervasi and Stefania Spina
Annotation of Multiword Expressions in the SUK 1.0 Training Corpus of Slovene: Lessons Learned and Future Steps
Jaka Čibej, Polona Gantar and Mija Bon
Light Verb Constructions in Universal Dependencies for South Asian Languages
Abishek Stephen and Daniel Zeman
Ad Hoc Compounds for Stance Detection
Qi Yu, Fabian Schlotterbeck, Henning Wang, Naomi Reichmann, Britta Stolterfoht, Regine Eckardt and Miriam Butt
Short Papers
MWE
A demonstration of MWE-Finder and MWE-Annotator
Jan Odijk, Martin Kroon, Tijmen Baarda, Ben Bonfil and Sheean Spoel
Annotating Compositionality Scores for Irish Noun Compounds is Hard Work
Abigail Walsh, Teresa Clifford, Emma Daly, Jane Dunne, Brian Davis and Gearóid Ó Cleircín
UD
Synthetic-Error Augmented Parsing of Swedish as a Second Language: Experiments with Word Order
Arianna Masciolini, Emilie Francis and Maria Irena Szawerna
A Universal Dependencies Treebank for Gujarati
Mayank Jobanputra, Maitrey Mehta and Çağrı Çöltekin
Overcoming Early Saturation on Low-Resource Languages in Multilingual Dependency Parsing
Jiannan Mao, Chenchen Ding, Hour Kaing, Hideki Tanaka, Masao Utiyama and Tadahiro Matsumoto.
Domain-Weighted Batch Sampling for Neural Dependency Parsing
Jacob Striebel, Daniel Dakota and Sandra Kübler
Redefining Syntactic and Morphological Tasks for Typologically Diverse Languages
Omer Goldman, Leonie Weissweiler and Reut Tsarfaty
MWE+UD
The Vedic Compound Dataset
Sven Sellmer and Oliver Hellwig
Diachronic Analysis of Multi-word Expression Functional Categories in Scientific English
Diego Alves, Stefania Degaetano-Ortlieb, Elena Schmidt and Elke Teich
Multiword Expressions between the Corpus and the Lexicon: Universality, Idiosyncrasy, and the Lexicon-Corpus Interface
Verginica Barbu Mititelu, Voula Giouli, Kilian Evang, Daniel Zeman, Petya Osenova, Carole Tiberius, Simon Krek, Stella Markantonatou, Ivelina Stoyanova, Ranka Stanković and Christian Chiarcos
Nonarchival Presentations
These papers are either already published elsewhere or work in progress. They
have not undergone MWE-UD 2024’s formal peer review progress and are not
included in the proceedings, only listed in the programme. Some nonarchival
presentations were included in the programme after the proceedings were
completed, and are thus not listed in the version of the programme that is
included in the proceedings.
MWE
MultiMWE: Building a Multi-lingual Multi-Word Expression (MWE) Parallel Corpora
Lifeng Han
AlphaMWE-Arabic: Arabic Edition of Multilingual Parallel Corpora with Multiword Expression Annotations
Najet Hadj Mohamed, Malak Rassem, Lifeng Han and Goran Nenadic
A demonstration of MWE-Finder and MWE-Annotator
Jan Odijk, Martin Kroon, Tijmen Baarda, Ben Bonfil and Sheean Spoel
Is Less More? Quality, Quantity and Context in Idiom Processing with Natural Language Models
Agne Knietaite, Adam Allsebrook, Anton Minkov, Adam Tomaszewski, Norbert Slinko, Richard Johnson, Thomas M. R. Pickard and Aline Villavicencio
Annotating Compositionality Scores for Irish Noun Compounds is Hard Work
Abigail Walsh, Teresa Clifford, Emma Daly, Jane Dunne, Brian Davis and Gearóid Ó Cleircín
UD
MaiBaam: A Multi-Dialectal Bavarian Universal Dependency Treebank
Verena Blaschke, Barbara Kovačić, Siyao Peng, Hinrich Schütze and Barbara Plank
Redefining Syntactic and Morphological Tasks for Typologically Diverse Languages
Omer Goldman, Leonie Weissweiler and Reut Tsarfaty
Sparse Logistic Regression with High-order Features for Automatic Grammar Rule Extraction from Treebanks
Santiago Herrera, Caio Corro and Sylvain Kahane
Joint Annotation of Morphology and Syntax in Dependency Treebanks
Bruno Guillaume, Kim Gerdes, Kirian Guiller, Sylvain Kahane and Yixuan Li
MWE+UD
A Corpus of Persian Sentences Annotated with Verbal Multiword Expressions: Development and Guidelines
Vahide Tajalli, Yaldasadat Yarandi, Mahtab sarlak, Mehrnoush Shamsfard and Arezoo Haghbin
UCxn: Typologically Informed Annotation of Constructions Atop Universal Dependencies
Leonie Weissweiler, Nina Böbel, Kirian Guiller, Santiago Herrera, Wesley Scivetti, Arthur Lorenzi, Nurit Melnik, Archna Bhatia, Hinrich Schütze, Lori Levin, Amir Zeldes, Joakim Nivre, William Croft and Nathan Schneider
Multiword expressions (MWEs) are word combinations that exhibit lexical, syntactic, semantic, pragmatic, and/or statistical idiosyncrasies (Baldwin and Kim, 2010), such as by and large, hot dog, pay a visit and pull someone’s leg. The notion encompasses closely related phenomena: idioms, compounds, light-verb constructions, phrasal verbs, rhetorical figures, collocations, institutionalized phrases, etc. Their behavior 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 modeling (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).
Universal Dependencies (UD; De Marneffe et al., 2021) is a framework for cross-linguistically consistent treebank annotation that has so far been applied to over 100 languages. The framework aims to capture similarities as well as idiosyncrasies among typologically different languages (e.g., morphologically rich languages, pro-drop languages, and languages featuring clitic doubling). The goal in developing UD was not only to support comparative evaluation and cross-lingual learning but also to facilitate multilingual natural language processing and enable comparative linguistic studies.
After independently running a successful series of workshops, the MWE and UD communities are now joining forces to organize a joint workshop. This is a timely collaboration because the two communities clearly have overlapping interests. For instance, while UD has several dependency relations that can be used to annotate MWEs, both annotation guidelines (i.e. is syntactic irregularity and inflexibility or semantic non-compositionality the leading criterion?) and annotation practice (both across treebanks for a single language and across languages) for these relations can be improved (Schneider and Zeldes, 2021). The PARSEME MWE-annotated corpora for 26 languages build on UD annotated corpora (Savary et al., 2023). Both communities share an interest in developing guidelines, data-sets, and tools that can be applied to a wide range of typologically diverse languages, raising fundamental questions about tokenization, lemmatization, and morphological decomposition of tokens. Proposals for harmonizing annotation practice between what has been achieved in PARSEME and UD and expanding PARSEME MWE annotation to non-verbal MWEs are also central to the recently started UniDive COST action (CA21167).
The workshop invites submissions of original research on MWE, UD, and the interplay of both. In particular, the following topics are especially relevant:
- Sensitivity of LLMs to MWE and syntactic dependencies. Studies along the lines of Manning et al. (2020) (UD), Nedumpozhimana and Kelleher (2021), Garcia et al. (2021), Fakharian and Cook (2021), Moreau et al. (2018) (MWE), and others on the question to what extent LLMs make use of syntactic dependencies or are capable of detecting MWEs and capturing their semantics.
- Applicability of UD and MWE annotation and discovery for low-resource and typologically diverse languages and language varieties. Both UD and PARSEME aim at universal applicability across a wide range of languages. Much theoretical, computational, and empirical work concentrates on high-resource languages however. Applying these frameworks to typologically diverse languages may lead one to reconsider the notion of token, word, and morphological segmentation, and to reassess the notion of MWE for languages that feature compounding or incorporation (Baldwin et al., 2021; Haspelmath, 2023).
- Case studies. Studies on the consistency, coverage or universal applicability of MWE annotation in the UD or PARSEME frameworks, as well as studies on automatic detection and interpretation of MWEs in corpora.
- MWE and UD processing to enhance end-user applications. MWEs have gained particular attention in end-user applications, including MT (Zaninello and Birch, 2020; Han et al., 2021), simplification (Kochmar et al., 2020), language learning and assessment (Paquot et al., 2019; Christiansen and 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.
- Testing developed systems on the latest dataset versions. Authors are also encouraged to submit papers that test the developed systems using the recent UD 2.13 and/or PARSEME 1.3 releases.
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-UD proceedings.
Papers should be submitted via the workshop’s START submission page. Please choose the appropriate submission format (archival/non-archival). Submissions must follow the LREC-COLING 2024 stylesheet.
When submitting a paper from the START page, authors will be asked to provide essential information about resources (in a broad sense, i.e. also technologies, standards, evaluation kits, etc.) that have been used for the research described in the paper or are a new result of the research. Moreover, ELRA encourages all LREC-COLING authors to share the described LRs (data, tools, services, etc.) to enable their reuse and replicability of experiments (including evaluation ones)
Archival papers with existing reviews from ACL Rolling Review (ARR)) will also be considered. A paper may not be under review through ARR and MWE-UD simultaneously. A paper that has or will receive reviews through ARR may not be submitted for review to MWE-UD.
- Best paper award criteria TBD
- UniDive members with accepted papers may be eligible for travel reimbursement via UniDive.
- Accepted authors from underrepresented groups (e.g., an underrepresented counry) or accepted authors of work on low-resource languages may be eligible to apply for an ACL-SIGLEX travel grant of upto 500 USD.
| What |
When |
| Paper submission deadline |
March 3, 2024 |
| ARR commitment deadline |
March 25, 2024 |
| Notification of acceptance |
April 1, 2024 |
| Camera-ready papers due |
April 8, 2024 |
| Underline upload deadline |
TBD |
| Workshop |
May 25, 2024 |
All deadlines are at 23:59 UTC-12 (Anywhere on Earth).
| Archna Bhatia | Institute for Human and Machine Cognition, USA |
| Gosse Bouma | Groningen University, NL |
| A. Seza Dogruöz | Ghent University, Belgium |
| Kilian Evang | Heinrich Heine University Düsseldorf, DE |
| Marcos Garcia | University of Santiago de Compostela, Galiza, Spain |
| Voula Giouli | Institute for Language & Speech Processing, ATHENA RC, Greece |
| Lifeng Han | Univ. of Manchester, UK |
| Joakim Nivre | Uppsala University and Research Institutes of Sweden, Sweden |
| Alexandre Rademaker | IBM Research, Brazil |
| Verginica Barbu Mititelu |
Romanian Academy |
| Cherifa Ben Kehlil |
University of Tours |
| Philippe Blache |
Aix-Marseille Uni |
| Francis Bond |
Palacký University |
| Claire Bonial |
U.S. Army Research Laboratory |
| Julia Bonn |
University of Colorado Boulder |
| Tiberiu Boroș |
Adobe |
| Marie Candito |
Université Paris Cité |
| Giuseppe G. A. Celano |
Leipzig Uni |
| Kenneth Church |
Baidu |
| Çağrı Çöltekin |
University of Tübingen |
| Mathieu Constant |
Université de Lorraine |
| Monika Czerepowicka |
University of Warmia and Mazury |
| Daniel Dakota |
Indiana University |
| Miryam de Lhoneux |
KU Leuven |
| Marie-Catherine de Marneffe |
UC Louvain |
| Valeria de Paiva |
Nuance |
| Gaël Dias |
University of Caen Basse-Normandie |
| Kaja Dobrovoljc |
University of Ljubljana |
| Rafael Ehren |
Heinrich Heine University Düsseldorf |
| Gülşen Eryiğit |
Istanbul Technical University |
| Meghdad Farahmand |
Berlin, Germany |
| Christiane Fellbaum |
Princeton University |
| Jennifer Foster |
Dublin City University |
| Aggeliki Fotopoulou |
Institute for Language and Speech Processing, ATHENA RC |
| Stefan Th. Gries |
UC Santa Barbara & JLU Giessen |
| Bruno Guillaume |
Université de Lorraine |
| Tunga Gungor |
Bogaziçi University |
| Eleonora Guzzi |
Universidade da Coruña |
| Laura Kallmeyer |
Heinrich Heine University Düsseldorf |
| Cvetana Krstev |
University of Belgrade |
| Timm Lichte |
University of Tübingen |
| Irina Lobzhanidze |
Ilia State University |
| Teresa Lynn |
ADAPT Centre |
| Stella Markantonatou |
Institute for Language & Speech Processing, ATHENA RC |
| John P. McCrae |
National University of Ireland, Galway |
| Nurit Melnik |
The Open University of Israel |
| Johanna Monti |
“L’Orientale” University of Naples |
| Dmitry Nikolaev |
University of Manchester |
| Jan Odijk |
University of Utrecht |
| Petya Osenova |
Bulgarian Academy of Sciences |
| Yannick Parmentier |
University of Lorraine |
| Agnieszka Patejuk |
University of Oxford and Institute of Computer Science, Polish Academy of Sciences |
| Pavel Pecina |
Charles University |
| Ted Pedersen |
University of Minnesota |
| Prokopis Prokopidis |
Institute for Language and Speech Processing, ATHENA RC |
| Manfred Sailer |
Goethe-Universität Frankfurt am Main |
| Tanja Samardžić |
University of Zurich |
| Agata Savary |
Université Paris-Saclay |
| Nathan Schneider |
Georgetown University |
| Sabine Schulte im Walde |
University of Stuttgart |
| Sebastian Schuster |
Saarland University |
| Matthew Shardlow |
University of Manchester |
| Joaquim Silva |
Universidade NOVA de Lisboa |
| Maria Simi |
Università di Pisa |
| Ranka Stanković |
University of Belgrade |
| Ivelina Stoyanova |
Bulgarian Academy of Sciences |
| Stan Szpakowicz |
University of Ottawa |
| Shiva Taslimipoor |
University of Cambridge |
| Beata Trawinski |
Leibniz Institute for the German Language |
| Ashwini Vaidya |
Indian Institute of Technology |
| Marion Di Marco |
Ludwig Maximilian University of Munich |
| Amir Zeldes |
Georgetown University |
| Daniel Zeman |
Charles University |
The workshop follows the LREC/COLING’s anti-harassment policy.
For any inquiries regarding the workshop, please send an email to the Organizing Committee at mweud2024-organizers@uni-duesseldorf.de.
Please register to SIGLEX and check the “MWE
Section” box to be registered to our mailing list.