UMRs in Boston Summer School – 2nd Call for Applications
June 9-13, 2025
Brandeis University, Massachusetts, USA
URL: https://umr4nlp.github.io/web/SummerSchool2025.html 

We invite applications for a five-day summer school on Uniform Meaning 
Representations (UMR).
Impressive progress has been made in many aspects of natural language 
processing (NLP) in recent years. Most notably, the achievements of 
transformer-based large language models such as ChatGPT would seem to obviate 
the need for any type of semantic representation beyond what can be encoded as 
contextualized word embeddings of surface text. Advances have been particularly 
notable in areas where large training data sets exist, and it is advantageous 
to build an end-to-end training architecture without resorting to intermediate 
representations. For any truly interactive NLP applications, however, a more 
complete understanding of the information conveyed by each sentence is needed 
to advance the state of the art. Here, "understanding'' entails the use of some 
form of meaning representation. NLP techniques that can accurately capture the 
required elements of the meaning of each utterance in a formal representation 
are critical to making progress in these areas and have long been a central 
goal of the field. As with end-to-end NLP applications, the dominant approach 
for deriving meaning representations from raw textual data is through the use 
of machine learning and appropriate training data. This allows the development 
of systems that can assign appropriate meaning representations to previously 
unseen text. 
In this five-day course, instructors from the University of Colorado and 
Brandeis University will describe the framework of Uniform Meaning 
Representations (UMRs), a recent cross-lingual, multi-sentence incarnation of 
Abstract Meaning Representations (AMRs), that addresses these issues and 
comprises such a transformative representation. Incorporating Named Entity 
tagging, discourse relations, intra-sentential coreference, negation and 
modality, and the popular PropBank-style predicate argument structures with 
semantic role labels into a single directed acyclic graph structure, UMR builds 
on AMR and keeps the essential characteristics of AMR while making it 
cross-lingual and extending it to be a document-level representation. It also 
adds aspect, multi-sentence coreference and temporal relations, and scope. Each 
day will include lectures and hands-on practice.

Topics to be covered may include the following, among others:
1.      The basic structural representation of UMR and its application to 
multiple languages;
2.      How UMR encodes different types of MWE (multi-word expressions), 
discourse and temporal relations, and TAM (tense-aspect-modality) information 
in multiple languages, and differences between AMR and UMR;
3.      Going from IGT (interlinear glossed text) to UMR graphs 
semi-automatically;
4.      Formal semantic interpretation of UMR incorporating a 
continuation-based semantics for scope phenomena involving modality, negation, 
and quantification;
5.      Extension to UMR for encoding gesture in multimodal dialogue, Gesture 
AMR (GAMR), which aligns with speech-based UMR to account for situated 
grounding in dialogue. 
6.      UMR parsing and applications

To apply, please complete this form by Nov. 15, 2024. 
https://www.colorado.edu/linguistics/umrs-boston-summer-school-application 
Other important dates:
●       Notification of acceptance:     Dec. 15, 2024
●       Confirmation of participation:  Jan. 31, 2025

Participation will be fully funded (reasonable airfare, lodging, and meals). 
This summer school has been made possible by funding from NSF Collaborative 
Research: Building a Broad Infrastructure for Uniform Meaning Representations 
(Award # 2213805), with additional support from Brandeis University.
_______________________________________________
Corpora mailing list -- [email protected]
https://list.elra.info/mailman3/postorius/lists/corpora.list.elra.info/
To unsubscribe send an email to [email protected]

Reply via email to