Matthias Lindemann


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Normalizing Compositional Structures Across Graphbanks
Lucia Donatelli | Jonas Groschwitz | Matthias Lindemann | Alexander Koller | Pia Weißenhorn
Proceedings of the 28th International Conference on Computational Linguistics

The emergence of a variety of graph-based meaning representations (MRs) has sparked an important conversation about how to adequately represent semantic structure. MRs exhibit structural differences that reflect different theoretical and design considerations, presenting challenges to uniform linguistic analysis and cross-framework semantic parsing. Here, we ask the question of which design differences between MRs are meaningful and semantically-rooted, and which are superficial. We present a methodology for normalizing discrepancies between MRs at the compositional level (Lindemann et al., 2019), finding that we can normalize the majority of divergent phenomena using linguistically-grounded rules. Our work significantly increases the match in compositional structure between MRs and improves multi-task learning (MTL) in a low-resource setting, serving as a proof of concept for future broad-scale cross-MR normalization.

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Fast semantic parsing with well-typedness guarantees
Matthias Lindemann | Jonas Groschwitz | Alexander Koller
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

AM dependency parsing is a linguistically principled method for neural semantic parsing with high accuracy across multiple graphbanks. It relies on a type system that models semantic valency but makes existing parsers slow. We describe an A* parser and a transition-based parser for AM dependency parsing which guarantee well-typedness and improve parsing speed by up to 3 orders of magnitude, while maintaining or improving accuracy.


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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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Compositional Semantic Parsing across Graphbanks
Matthias Lindemann | Jonas Groschwitz | Alexander Koller
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Most semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. We present a compositional neural semantic parser which achieves, for the first time, competitive accuracies across a diverse range of graphbanks. Incorporating BERT embeddings and multi-task learning improves the accuracy further, setting new states of the art on DM, PAS, PSD, AMR 2015 and EDS.

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Verb-Second Effect on Quantifier Scope Interpretation
Asad Sayeed | Matthias Lindemann | Vera Demberg
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

Sentences like “Every child climbed a tree” have at least two interpretations depending on the precedence order of the universal quantifier and the indefinite. Previous experimental work explores the role that different mechanisms such as semantic reanalysis and world knowledge may have in enabling each interpretation. This paper discusses a web-based task that uses the verb-second characteristic of German main clauses to estimate the influence of word order variation over world knowledge.


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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.