Meaghan Fowlie


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AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suite
Jonas Groschwitz | Shay Cohen | Lucia Donatelli | Meaghan Fowlie
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

We present the Granular AMR Parsing Evaluation Suite (GrAPES), a challenge set for Abstract Meaning Representation (AMR) parsing with accompanying evaluation metrics. AMR parsers now obtain high scores on the standard AMR evaluation metric Smatch, close to or even above reported inter-annotator agreement. But that does not mean that AMR parsing is solved; in fact, human evaluation in previous work indicates that current parsers still quite frequently make errors on node labels or graph structure that substantially distort sentence meaning. Here, we provide an evaluation suite that tests AMR parsers on a range of phenomena of practical, technical, and linguistic interest. Our 36 categories range from seen and unseen labels, to structural generalization, to coreference. GrAPES reveals in depth the abilities and shortcomings of current AMR parsers.


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Learning compositional structures for semantic graph parsing
Jonas Groschwitz | Meaghan Fowlie | Alexander Koller
Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021)

AM dependency parsing is a method for neural semantic graph parsing that exploits the principle of compositionality. While AM dependency parsers have been shown to be fast and accurate across several graphbanks, they require explicit annotations of the compositional tree structures for training. In the past, these were obtained using complex graphbank-specific heuristics written by experts. Here we show how they can instead be trained directly on the graphs with a neural latent-variable model, drastically reducing the amount and complexity of manual heuristics. We demonstrate that our model picks up on several linguistic phenomena on its own and achieves comparable accuracy to supervised training, greatly facilitating the use of AM dependency parsing for new sembanks.


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Saarland at MRP 2019: Compositional parsing across all graphbanks
Lucia Donatelli | Meaghan Fowlie | Jonas Groschwitz | Alexander Koller | Matthias Lindemann | Mario Mina | Pia Weißenhorn
Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning

We describe the Saarland University submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference on Computational Natural Language Learning (CoNLL).


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AMR dependency parsing with a typed semantic algebra
Jonas Groschwitz | Matthias Lindemann | Meaghan Fowlie | Mark Johnson | Alexander Koller
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency tree parsing, constrained by a linguistically principled type system. We present two approximative decoding algorithms, which achieve state-of-the-art accuracy and outperform strong baselines.


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Parsing Minimalist Languages with Interpreted Regular Tree Grammars
Meaghan Fowlie | Alexander Koller
Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms

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A constrained graph algebra for semantic parsing with AMRs
Jonas Groschwitz | Meaghan Fowlie | Mark Johnson | Alexander Koller
Proceedings of the 12th International Conference on Computational Semantics (IWCS) — Long papers


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Order and Optionality: Minimalist Grammars with Adjunction
Meaghan Fowlie
Proceedings of the 13th Meeting on the Mathematics of Language (MoL 13)