| Challenge: | Semantic parsing offers many opportunities to improve natural language understanding . current research on open-domain semantic parsers focuses on supervised learning methods . |
| Approach: | They propose a semantically annotated parallel corpus for English, German, Italian, and Dutch . they use a matching tool to evaluate scoped meaning representations to match clauses . |
| Outcome: | The proposed method captures the semantics of negation, modals, quantification, and presupposition triggers . it compares scoped meaning representations to gold standard parsers and finds improvements . |
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| Challenge: | Empirical results suggest that the proposed methodology can be meaningfully applied to parsing into graph-structured target representations, uncovering hitherto unknown properties of the different approaches. |
| Approach: | They propose to map from natural language utterances to graph-based encodings of its semantic structure using contrastive and diagnostic evaluation techniques. |
| Outcome: | The proposed method can be meaningfully applied to parsing into graph-structured target representations, uncovering hitherto unknown properties of the different systems that can inform future development and cross-fertilization across approaches. |
Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics (2020.coling-main)
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| Challenge: | a systematic comparative analysis of linguistic meaning representations from different frameworks is needed. |
| Approach: | They compare a rule-based converter and a supervised delexicalized parser to map meaning representations from different frameworks. |
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Cross-Lingual Abstract Meaning Representation Parsing (N18-1)
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| Challenge: | Abstract Meaning Representation (AMR) research has focused on English . Qualitative analysis shows that the new parsers overcome structural differences between the languages. |
| Approach: | They propose to use an AMR parser for English and parallel corpora to learn AMR for Italian, Spanish, German and Chinese. |
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A Survey of Meaning Representations – From Theory to Practical Utility (2024.naacl-long)
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| Challenge: | Symbolic meaning representations of natural language text have been studied since at least the 1960s . with the availability of large annotated corpora, the field has recently seen several new developments . |
| Approach: | They propose a framework for expressing meaning in natural language text using annotated corpora and a set of tools for machine learning. |
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Abstract Meaning Representation for Paraphrase Detection (N18-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsing is ideal for paraphrase detection . it abstracts away from the syntactic realization of a sentence, and denotes only its meaning in a canonical form. |
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Benchmarking Meaning Representations in Neural Semantic Parsing (2020.emnlp-main)
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| Challenge: | Existing work on meaning representations is not comprehensively evaluated due to the lack of readily-available execution engines. |
| Approach: | They propose a unified benchmark on meaning representations by integrating existing semantic parsing datasets, completing the missing logical forms, and implementing the missing execution engines. |
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Abstract Meaning Representation of Constructions: The More We Include, the Better the Representation (L18-1)
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Claire Bonial, Bianca Badarau, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Tim O’Gorman, Martha Palmer, Nathan Schneider
| Challenge: | Abstract Meaning Representation (AMR) uses a flexible pattern or template of multiple lexical items to provide semantic representation of certain constructions. |
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World Knowledge for Abstract Meaning Representation Parsing (L18-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsers are based on annotated graphs, but there is still room for improvement . |
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Parsing into Variable-in-situ Logico-Semantic Graphs (2020.acl-main)
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| Challenge: | a new type of graph-based meaning representation allows analysis for scope-related phenomena. |
| Approach: | They propose variable-in-situ logico-semantic graphs to bridge gap between semantic graph and logical form parsing. |
| Outcome: | The proposed graph-based meaning representation achieves 92.39% accuracy in terms of elementary dependency match . the output of the proposed parser is highly coherent . |
Towards Explainable Evaluation of Language Models on the Semantic Similarity of Visual Concepts (2022.coling-1)
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Maria Lymperaiou, George Manoliadis, Orfeas Menis Mastromichalakis, Edmund G. Dervakos, Giorgos Stamou
| Challenge: | Recent advances in NLP research have focused on robustness and explainability issues of their evaluation strategies. |
| Approach: | They propose to use pre-trained transformers to evaluate semantic similarity for visual vocabularies . they propose to provide explainable metrics for understanding the quality of retrieved instances . |
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