# A Logic-Based Framework for Natural Language Inference in Dutch

arXiv: https://arxiv.org/abs/2110.03323 · HTML: https://mlsanity.com/paper/2110.03323/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 49 references that are in the map.
- First posted on arXiv 7 Oct 2021.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference](https://mlsanity.com/paper/1902.01007/) (2019)
- [Annotation Artifacts in Natural Language Inference Data](https://mlsanity.com/paper/1803.02324/) (2018)
- [Hypothesis Only Baselines in Natural Language Inference](https://mlsanity.com/paper/1805.01042/) (2018)
- [Learning Latent Permutations with Gumbel-Sinkhorn Networks](https://mlsanity.com/paper/1802.08665/) (2018)
- [Breaking NLI Systems with Sentences that Require Simple Lexical Inferences](https://mlsanity.com/paper/1805.02266/) (2018)
- [RobBERT: a Dutch RoBERTa-based Language Model](https://mlsanity.com/paper/2001.06286/) (2020)
- [Probing Natural Language Inference Models through Semantic Fragments](https://mlsanity.com/paper/1909.07521/) (2019)
- [Performance Impact Caused by Hidden Bias of Training Data for Recognizing Textual Entailment](https://mlsanity.com/paper/1804.08117/) (2018)
- [Can neural networks understand monotonicity reasoning?](https://mlsanity.com/paper/1906.06448/) (2019)
- [The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations](https://mlsanity.com/paper/1702.03964/) (2017)
- [HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning](https://mlsanity.com/paper/1904.12166/) (2019)
- [SICKNL: A Dataset for Dutch Natural Language Inference](https://mlsanity.com/paper/2101.05716/) (2021)
- [Behavior Analysis of NLI Models: Uncovering the Influence of Three Factors on Robustness](https://mlsanity.com/paper/1805.04212/) (2018)
- [LangPro: Natural Language Theorem Prover](https://mlsanity.com/paper/1708.09417/) (2017)
- [Exploring Transitivity in Neural NLI Models through Veridicality](https://mlsanity.com/paper/2101.10713/) (2021)
- [Acquisition of Phrase Correspondences using Natural Deduction Proofs](https://mlsanity.com/paper/1804.07656/) (2018)
- [Neural Proof Nets](https://mlsanity.com/paper/2009.12702/) (2020)
- [Learning as Abduction: Trainable Natural Logic Theorem Prover for Natural Language Inference](https://mlsanity.com/paper/2010.15909/) (2020)
- [Constructive Type-Logical Supertagging with Self-Attention Networks](https://mlsanity.com/paper/1905.13418/) (2019)
- [AE THEL: Automatically Extracted Typelogical Derivations for Dutch](https://mlsanity.com/paper/1912.12635/) (2019)

## Abstract (arXiv)

We present a framework for deriving inference relations between Dutch sentence pairs. The proposed framework relies on logic-based reasoning to produce inspectable proofs leading up to inference labels; its judgements are therefore transparent and formally verifiable. At its core, the system is powered by two λ-calculi, used as syntactic and semantic theories, respectively. Sentences are first converted to syntactic proofs and terms of the linear λ-calculus using a choice of two parsers: an Alpino-based pipeline, and Neural Proof Nets. The syntactic terms are then converted to semantic terms of the simply typed λ-calculus, via a set of hand designed type- and term-level transformations. Pairs of semantic terms are then fed to an automated theorem prover for natural logic which reasons with them while using the lexical relations found in the Open Dutch WordNet. We evaluate the reasoning pipeline on the recently created Dutch natural language inference dataset, and achieve promising results, remaining only within a 1.1-3.2% performance margin to strong neural baselines. To the best of our knowledge, the reasoning pipeline is the first logic-based system for Dutch.