Meaning Representations for Natural Languages: Design, Models and Applications
Julia Bonn, Jeffrey Flanigan, Jan Hajič, Ishan Jindal, Yunyao Li, Nianwen Xue
Abstract
This tutorial reviews the design of common meaning representations, SoTA models for predicting meaning representations, and the applications of meaning representations in a wide range of downstream NLP tasks and real-world applications. Reporting by a diverse team of NLP researchers from academia and industry with extensive experience in designing, building and using meaning representations, our tutorial has three components: (1) an introduction to common meaning representations, including basic concepts and design challenges; (2) a review of SoTA methods on building models for meaning representations; and (3) an overview of applications of meaning representations in downstream NLP tasks and real-world applications. We propose a cutting-edge, full-day tutorial for all stakeholders in the AI community, including NLP researchers, domain-specific practitioners, and students- Anthology ID:
- 2024.lrec-tutorials.3
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024): Tutorial Summaries
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Roman Klinger, Naozaki Okazaki, Nicoletta Calzolari, Min-Yen Kan
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 13–18
- Language:
- URL:
- https://aclanthology.org/2024.lrec-tutorials.3
- DOI:
- Cite (ACL):
- Julia Bonn, Jeffrey Flanigan, Jan Hajič, Ishan Jindal, Yunyao Li, and Nianwen Xue. 2024. Meaning Representations for Natural Languages: Design, Models and Applications. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024): Tutorial Summaries, pages 13–18, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Meaning Representations for Natural Languages: Design, Models and Applications (Bonn et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2024.lrec-tutorials.3.pdf