Michael Moortgat

Also published as: M. Moortgat


2021

pdf bib
SICK-NL: A Dataset for Dutch Natural Language Inference
Gijs Wijnholds | Michael Moortgat
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

We present SICK-NL (read: signal), a dataset targeting Natural Language Inference in Dutch. SICK-NL is obtained by translating the SICK dataset of (Marelli et al., 2014) from English into Dutch. Having a parallel inference dataset allows us to compare both monolingual and multilingual NLP models for English and Dutch on the two tasks. In the paper, we motivate and detail the translation process, perform a baseline evaluation on both the original SICK dataset and its Dutch incarnation SICK-NL, taking inspiration from Dutch skipgram embeddings and contextualised embedding models. In addition, we encapsulate two phenomena encountered in the translation to formulate stress tests and verify how well the Dutch models capture syntactic restructurings that do not affect semantics. Our main finding is all models perform worse on SICK-NL than on SICK, indicating that the Dutch dataset is more challenging than the English original. Results on the stress tests show that models don’t fully capture word order freedom in Dutch, warranting future systematic studies.

2020

pdf bib
Neural Proof Nets
Konstantinos Kogkalidis | Michael Moortgat | Richard Moot
Proceedings of the 24th Conference on Computational Natural Language Learning

Linear logic and the linear λ-calculus have a long standing tradition in the study of natural language form and meaning. Among the proof calculi of linear logic, proof nets are of particular interest, offering an attractive geometric representation of derivations that is unburdened by the bureaucratic complications of conventional prooftheoretic formats. Building on recent advances in set-theoretic learning, we propose a neural variant of proof nets based on Sinkhorn networks, which allows us to translate parsing as the problem of extracting syntactic primitives and permuting them into alignment. Our methodology induces a batch-efficient, end-to-end differentiable architecture that actualizes a formally grounded yet highly efficient neuro-symbolic parser. We test our approach on ÆThel, a dataset of type-logical derivations for written Dutch, where it manages to correctly transcribe raw text sentences into proofs and terms of the linear λ-calculus with an accuracy of as high as 70%.

pdf bib
ÆTHEL: Automatically Extracted Typelogical Derivations for Dutch
Konstantinos Kogkalidis | Michael Moortgat | Richard Moot
Proceedings of the 12th Language Resources and Evaluation Conference

We present ÆTHEL, a semantic compositionality dataset for written Dutch. ÆTHEL consists of two parts. First, it contains a lexicon of supertags for about 900 000 words in context. The supertags correspond to types of the simply typed linear lambda-calculus, enhanced with dependency decorations that capture grammatical roles supplementary to function-argument structures. On the basis of these types, ÆTHEL further provides 72 192 validated derivations, presented in four formats: natural-deduction and sequent-style proofs, linear logic proofnets and the associated programs (lambda terms) for meaning composition. ÆTHEL’s types and derivations are obtained by means of an extraction algorithm applied to the syntactic analyses of LASSY Small, the gold standard corpus of written Dutch. We discuss the extraction algorithm and show how ‘virtual elements’ in the original LASSY annotation of unbounded dependencies and coordination phenomena give rise to higher-order types. We suggest some example usecases highlighting the benefits of a type-driven approach at the syntax semantics interface. The following resources are open-sourced with ÆTHEL: the lexical mappings between words and types, a subset of the dataset consisting of 7 924 semantic parses, and the Python code that implements the extraction algorithm.

2019

pdf bib
Constructive Type-Logical Supertagging With Self-Attention Networks
Konstantinos Kogkalidis | Michael Moortgat | Tejaswini Deoskar
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)

We propose a novel application of self-attention networks towards grammar induction. We present an attention-based supertagger for a refined type-logical grammar, trained on constructing types inductively. In addition to achieving a high overall type accuracy, our model is able to learn the syntax of the grammar’s type system along with its denotational semantics. This lifts the closed world assumption commonly made by lexicalized grammar supertaggers, greatly enhancing its generalization potential. This is evidenced both by its adequate accuracy over sparse word types and its ability to correctly construct complex types never seen during training, which, to the best of our knowledge, was as of yet unaccomplished.

2004

pdf bib
Categorial Type Logic meets Dependency Grammar to annotate an Italian corpus
R. Bernardi | A. Bolognesi | F. Tamburini | M. Moortgat
Proceedings of the Workshop on Recent Advances in Dependency Grammar

2002

pdf bib
Syntactic Analysis in the Spoken Dutch Corpus (CGN)
Ton van der Wouden | Heleen Hoekstra | Michael Moortgat | Bram Renmans | Ineke Schuurman
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

pdf bib
Experiences from the Spoken Dutch Corpus Project
Nelleke Oostdijk | Wim Goedertier | Frank van Eynde | Louis Boves | Jean-Pierre Martens | Michael Moortgat | Harald Baayen
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

pdf bib
Using the Spoken Dutch Corpus for type-logical grammar induction
Michael Moortgat | Richard Moot
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

1993

pdf bib
Sixth Conference of the European Chapter of the Association for Computational Linguistics
Steven Krauwer | Michael Moortgat | Louis des Tombe
Sixth Conference of the European Chapter of the Association for Computational Linguistics