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SICKNL: A Dataset for Dutch Natural Language Inference

arXiv 2101.05716 ↗First posted 14 Jan 2021cs.CLcited by 20

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Abstract (arXiv)

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.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 14 Jan 2021.

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11 of 21 references.

References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,665
  3. 2015A large annotated corpus for learning natural language inferencecited by 1,837
  4. 2018XNLI: Evaluating Cross-lingual Sentence Representationscited by 913
  5. 2019Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inferencecited by 864
  6. 2018Stress Test Evaluation for Natural Language Inferencecited by 211
  7. 2019BERTje: A Dutch BERT Modelcited by 109
  8. 2020RobBERT: a Dutch RoBERTa-based Language Modelcited by 88
  9. 2019The merits of Universal Language Model Fine-tuning for Small Datasets -- a case with Dutch book reviewscited by 14
  10. 2016Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resourcecited by 7
  11. 2020Binary and Multitask Classification Model for Dutch Anaphora Resolution: Die/Dat Predictioncited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 5; 2023: 4; 2024: 3; 2025: 3; 2026: 2.

20 papers.

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  1. 2024Zero-Shot Tokenizer Transfercited by 30
  2. 2022Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embeddingcited by 25
  3. 2021IndoNLI: A Natural Language Inference Dataset for Indonesiancited by 24
  4. 2022Compositional Evaluation on Japanese Textual Entailment and Similaritycited by 14
  5. 2023Tik-to-Tok: Translating Language Models One Token at a Time: An Embedding Initialization Strategy for Efficient Language Adaptationcited by 11
  6. 2023DUMB: A Benchmark for Smart Evaluation of Dutch Modelscited by 6
  7. 2025MTEB-NL and E5-NL: Embedding Benchmark and Models for Dutchcited by 5
  8. 2021Improving BERT Pretraining with Syntactic Supervisioncited by 4
  9. 2022RobBERTje: a Distilled Dutch BERT Modelcited by 4
  10. 2022Discontinuous Constituency and BERT: A Case Study of Dutchcited by 3
  11. 2023Structural Ambiguity and its Disambiguation in Language Model Based Parsers: the Case of Dutch Clause Relativizationcited by 2
  12. 2024Modular Sentence Encoders: Separating Language Specialization from Cross-Lingual Alignmentcited by 2
8 more
  1. 2022Improving Stance Detection by Leveraging Measurement Knowledge from Social Sciences: A Case Study of Dutch Political Tweets and Traditional Gender Role Divisioncited by 1
  2. 2023JCSE: Contrastive Learning of Japanese Sentence Embeddings and Its Applicationscited by 1
  3. 2024HNCSE: Advancing Sentence Embeddings via Hybrid Contrastive Learning with Hard Negativescited by 1
  4. 2025Domain Adaptation for Japanese Sentence Embeddings with Contrastive Learning based on Synthetic Sentence Generationcited by 1
  5. 2021A Logic-Based Framework for Natural Language Inference in Dutchcited by 0
  6. 2025Dataset Creation for Visual Entailment using Generative AIcited by 0
  7. 2026SEA-NLI: Natural Language Inference as a Lens into Southeast Asian Cultural Understandingcited by 0
  8. 2026Translation as a Computationally Efficient Bridge: Feasibility of English BERT for Low-Resource Languagescited by 0