Papers by Jonas Groschwitz
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. |
AMR dependency parsing with a typed semantic algebra (P18-1)
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| Challenge: | Abstract Meaning Representations (AMRs) are graphs which describe the predicate-argument structure of a sentence. |
| Approach: | They propose a semantic parser which parses strings into tree representations of the compositional structure of an AMR graph. |
| Outcome: | The proposed parser outperforms baselines and standard neural techniques for supertagging and dependency tree parsing. |
AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suite (2023.emnlp-main)
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| Challenge: | Abstract Meaning Representation parsers have improved in recent years, but not solved. |
| Approach: | They propose an evaluation suite that evaluates AMR parsers on a range of phenomena . they find that current parser outputs are far from satisfactory . |
| Outcome: | The proposed evaluation suite reveals the abilities and shortcomings of current parsers. |
Scope-enhanced Compositional Semantic Parsing for DRT (2024.emnlp-main)
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| Challenge: | Existing compositional semantic parsers for DRT struggle to produce well-formed representations due to the complexity of the sentence. |
| Approach: | They propose a compositional, neurosymbolic semantic parser for DRT that uses a novel mechanism for predicting quantifier scope. |
| Outcome: | The proposed model produces well-formed outputs and performs well on complex sentences. |
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. |
A Corpus of German Abstract Meaning Representation (DeAMR) (2024.lrec-main)
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| Challenge: | Abstract Meaning Representations (AMRs) are semantic graphs that abstract away from surface syntax and capture the meaning of who does what to whom in a sentence. |
| Approach: | They propose to use German Abstract Meaning Representation (Deutsche AMR) to represent the structure and semantics of German. |
| Outcome: | The proposed framework is based on an annotated corpus of 400 DeAMR in German and is validated through inter-annotator agreement. |
Fast semantic parsing with well-typedness guarantees (2020.emnlp-main)
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| Challenge: | Existing algorithms for AM dependency parsing are slow and do not support linguistic principles. |
| Approach: | They propose an A* parser and a transition-based parsing algorithm which guarantee well-typedness and improve parse speed by up to 3 orders of magnitude. |
| Outcome: | The proposed algorithms guarantee well-typedness and improve parsing speed by up to 3 orders of magnitude while maintaining or improving accuracy. |