[HPSG-L] First Call for Papers: LREC 2026 Workshop on Learning Non-Literal Expressions with Small Data

[Valia Kordoni]

Workshop on Learning Non-Literal Expressions with Small Data

To be held in conjunction with LREC 2026, Palma de Mallorca, Spain on 11 
May 2026.

Overview
Non-Literal Expressions (NLEs) in natural language are a reflection of 
fundamental cognitive processes such as analogical reasoning and 
categorisation, and are deeply rooted in everyday communication. NLEs 
understanding is therefore an essential task for language modeling. This 
task is especially challenging because it cannot be tackled by falling 
back on individual word meanings, but requires taking into account 
larger chunks of surrounding text or even contextual information. At the 
same time, it is important because the reliable processing of NLEs is 
relevant for optimizing downstream tasks like translation and 
summarization.

This workshop focuses on understanding of Non-Literal Expressions. While 
most of the earlier work on NLEs had been devoted to metaphor and 
metonymy, recent activities target other forms of NLEs as well, e.g., 
hyperbole (deliberate exaggeration), litotes (understatement), 
rhetorical questions, and irony. Humanly annotated corpora for NLEs have 
very recently started becoming available to the research community and 
may serve as the basis for data-driven approaches to NLEs processing, 
with the interrelated goals of first identifying and then interpreting 
such expressions. Such data is mostly of high linguistic quality, but 
still very limited in size. Thus, the workshop’s focus is on adaptation 
of Language Models (LMs) and Deep Learning (DL) for processing of 
Non-Literal Expressions with limited high-quality data, since such 
constructs still pose big identification and processing challenges in 
natural language analysis tasks.

Topics of Interest
We are interested in contributions which focus on the use of techniques 
like self-training for leveraging unlabelled data, as well as in work 
that focuses on the incorporation of external linguistic resources and 
knowledge injection to enrich features, and also in research that 
describes work on utilisation of multitask learning with the aim to 
benefit from related tasks.

The workshop also wants to discuss alternative approaches which may 
elaborate on the use of pre-trained Language Models (LMs) as a 
foundation and the application of techniques like contrastive learning 
and clustering to identify challenging examples within the data, the 
ultimate aim of the workshop being to highlight the necessity of 
high-quality data, as well as cross-lingual datasets.

Invited Speakers

- Prof. Barbara Plank, LMU Munich (https://bplank.github.io/)

- Dr. Debanjan Ghosh, Princeton, USA

Details will be announced on the workshop website (tba).

Submission Guidelines
Papers must be submitted electronically through Softconf: [link to 
come]. Submissions should:
•    Be 4–8 pages, excluding references and optional Ethics Statements
•    Follow the LREC 2026 style guidelines, available on the conference 
website: https://lrec2026.info/authors-kit/
•    Use templates provided here: 
https://lrec2026.info/calls/second-call-for-papers/

Authors will be asked to supply information on any language resources 
(broadly defined — data, tools, standards, evaluation sets, etc.) used 
in or resulting from their work. ELRA strongly encourages sharing such 
resources to support reproducibility and reuse.

Accepted papers will appear in the workshop proceedings. Presentation 
format (oral/poster) will be based solely on how best to communicate the 
work.

Important Dates
•    20 February 2026 — Submission Deadline
•    11 March 2026 — Notification of Acceptance
•    28 March 2026 — Camera-ready Papers Due

Endorsements
The workshop is endorsed by: Collaborative Research Centre 1412 
"REGISTER" funded by the DFG Deutsche Forschungsgemeinschaft (German 
Research Foundation)

Organizers
•    Markus Egg — Humboldt-Universität zu Berlin, Germany
•    Valia Kordoni - Humboldt-Universität zu Berlin, Germany

Contact: kordonie at rz.hu-berlin.de