Papers with UCCA
RepGraph: Visualising and Analysing Meaning Representation Graphs (2021.emnlp-demo)
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| Challenge: | Graph-based meaning representations provide rich semantic annotations, but visualising them clearly is more challenging than for fully lexicalized representations. |
| Approach: | They propose to use RepGraph to visualise, manipulate and analyse semantically parsed graph data in a JSON-based serialisation format. |
| Outcome: | The proposed visualisation and analysis tool supports DMRS, EDS, PTG, UCCA, and AMR semantic frameworks. |
Turkish Universal Conceptual Cognitive Annotation (2022.lrec-1)
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| Challenge: | UCCA-annotated datasets have been released in English, French, and German . a semi-automatic annotation approach is used to annotate the datasets . |
| Approach: | They propose to use an external semantic parser to annotate Turkish sentences . they use the same parsers for evaluation purposes and conducted experiments . |
| Outcome: | The proposed dataset is the first UCCA-annotated Turkish dataset . the results show that the parser can improve on the initial annotations . |
Content Differences in Syntactic and Semantic Representation (N19-1)
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| Challenge: | Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate. |
| Approach: | They propose to use Universal Dependencies and UCCA as test cases to compare syntactic and semantic schemes. |
| Outcome: | The proposed comparison methodology can be used for fine-grained evaluation of UCCA parsing, highlighting both challenges and potential sources for improvement. |
Broad-Coverage Semantic Parsing as Transduction (D19-1)
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| Challenge: | Existing approaches to broad-coverage semantic parsing are not applicable to all frameworks because of the lack of explicit alignments between tokens in the sentence and nodes in the semantic graph. |
| Approach: | They propose a transduction parsing paradigm that unifies different broad-coverage semantic parsers into a paradigm that leverages multiple attention mechanisms to build meaning representation. |
| Outcome: | The proposed approach improves state-of-the-art on AMR, SDP and UCCA and is competitive with the state- of-the art on SDP. |