Meaning Representations for Natural Languages: Design, Models and Applications (2024.lrec-tutorials)
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| Challenge: | a tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. |
| Approach: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. authors propose a cutting-edge, full-day tutorial for all stakeholders in the AI community. |
| Outcome: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models . it also reviews the applications of meaning representation in downstream NLP tasks and real-world applications . |
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Meaning Representations for Natural Languages: Design, Models and Applications (2022.emnlp-tutorials)
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| Challenge: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models. |
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A Survey of Meaning Representations – From Theory to Practical Utility (2024.naacl-long)
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On the Importance of Distinguishing Word Meaning Representations: A Case Study on Reverse Dictionary Mapping (N19-1)
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| Challenge: | introductory tutorial to UCCA, a symbolic meaning representation for semantic representations. |
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| Challenge: | linguistic variation allows for the expression of social meaning, information about the social background and identity of the language user. |
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| Challenge: | Neural networks are the state-of-the-art method of machine learning for many problems in NLP. |
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