This paper addresses structural ambiguity in Dutch relative clauses.By investigating the task of disambiguation by grounding, we study how the presence of a prior sentence can resolve relative clause ambiguities.We apply this method to two parsing architectures in an attempt to demystify the parsing and language model components of two present-day neural parsers.Results show that a neurosymbolic parser, based on proof nets, is more open to data bias correction than an approach based on universal dependencies, although both setups suffer from a comparable initial data bias.

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.

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 typelogical derivations for written Dutch, where it manages to correctly transcribe raw text sentences into proofs and terms of the linear λcalculus with an accuracy of as high as 70%.

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.