Call for Participation: Shared Task in Parsing into UMR
Please consider participating in shared task in multilingual parsing
into Uniform Meaning Representation. Details and registration link here:
https://ufal.mff.cuni.cz/umr-parsing
The shared task is part of the DMR 2026 workshop, see the call for
papers below:
Call for Papers: DMR 2026
DMR 2026 invites the submissions of long and short papers about original
works on the design, processing, and use of meaning representations.
While deep learning methods have led to many breakthroughs in practical
natural language applications, there is still a sense among many NLP
researchers that we have a long way to go before we can develop systems
that can actually “understand” human language and explain the decisions
they make. Indeed, “understanding” natural language entails many
different human-like capabilities, and they include but are not limited
to the ability to track entities in a text, understand the relations
between these entities, track events and their participants described in
a text, understand how events unfold in time, and distinguish events
that have actually happened from events that are planned or intended,
are uncertain, or did not happen at all. We believe a critical step in
achieving natural language understanding is to design meaning
representations for text that have the necessary meaning “ingredients”
that help us achieve these capabilities. Such meaning representations
can also potentially be used to evaluate the compositional
generalization capacity of deep learning models.
There has been a growing body of research devoted to the design,
annotation, and parsing of meaning representations in recent years. In
particular, formal meaning representation frameworks such as Minimal
Recursion Semantics (MRS) and Discourse Representation Theory are
developed with the goal of supporting logical inference in
reasoning-based AI systems and are therefore easily translatable into
first-order logic, while other meaning representation frameworks such as
Abstract Meaning Representation (AMR), Uniform Meaning Representation
(UMR), Tecto-grammatical Representation (TR) in Prague Dependency
Treebanks and the Universal Conceptual Cognitive Annotation (UCCA), put
more emphasis on the representation of core predicate-argument
structure. The automatic parsing of natural language text into these
meaning representations and the generation of natural language text from
these meaning representations are also very active areas of research,
and a wide range of technical approaches and learning methods have been
applied to these problems.
DMR intends to bring together researchers who are producers and
consumers of meaning representations and, through their interaction,
gain a deeper understanding of the key elements of meaning
representations that are the most valuable to the NLP community. The
workshop will provide an opportunity for meaning representation
researchers to present new frameworks and to critically examine existing
frameworks with the goal of using their findings to inform the design of
next-generation meaning representations. One particular goal is to
understand the relationship between distributed meaning representations
trained on large data sets using network models and the symbolic meaning
representations that are carefully designed and annotated by NLP
researchers, with an aim of gaining a deeper understanding of areas
where each type of meaning representation is the most effective.
The workshop solicits papers that address one or more of the following
topics:
* Development and annotation of meaning representations;
* Challenges and techniques in leveraging meaning representations for
downstream applications, including neuro-symbolic approaches;
* The relationship between symbolic meaning representations and
distributed semantic representations;
* Issues in applying meaning representations to multilingual settings
and lower-resourced languages;
* Challenges and techniques in automatic parsing of meaning
representations;
* Challenges and techniques in automatically generating text from
meaning representations;
* Meaning representation evaluation metrics;
* Cross-framework comparison of meaning representations and their
formal properties;
* Any other topics that address the design, processing, and use of
meaning representations.
Contact
For any questions regarding the workshop, please contact us
[email protected].
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