Papers with graph-based meaning representations
SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Features (2022.aacl-main)
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| Challenge: | Abstract Meaning Representation (S3BERT) embeddings are composed of explainable sub-embeddings that emphasize various sentence meaning features. |
| Approach: | They propose to induce Semantically Structured Sentence BERT embeddings (S3BERT) that emphasize various sentence meaning features. |
| Outcome: | The proposed model shows high correlation to human similarity ratings, but lacks interpretability. |
Anchor and Broadcast: An Efficient Concept Alignment Approach for Evaluation of Semantic Graphs (2024.lrec-main)
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| Challenge: | Abstract Meaning Representation (AMR) is a sentencelevel formalism designed for English. |
| Approach: | They present an intuitive tool for evaluating graph-based meaning representations . they use an anchor broadcast alignment algorithm that is not subject to local maxima . |
| Outcome: | The proposed tool is highly correlated with the widely used Smatch score, but computation takes only about 40% the time. |
Normalizing Compositional Structures Across Graphbanks (2020.coling-main)
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| Challenge: | Graph-based meaning representations (MRs) exhibit structural differences that reflect different theoretical and design considerations, presenting challenges to uniform linguistic analysis and cross-framework semantic parsing. |
| Approach: | They propose a method to normalize MRs at the compositional level by linguistically-grounded rules. |
| Outcome: | The proposed method increases the match in compositional structure between MRs and improves multi-task learning in a low-resource setting. |
Compositional Semantic Parsing across Graphbanks (P19-1)
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| Challenge: | Existing semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. |
| Approach: | They propose a compositional neural semantic parser which achieves competitive accuracies across graphbanks. |
| Outcome: | The proposed system achieves competitive accuracies across a variety of graphbanks. |