[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/cited-by/nizamani-2024-siga-naturalistic-nli-dataset-english/)

[home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [cited-by](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/) / Who cites «SIGA: A Naturalistic NLI Dataset of English Scalar Implicatures with Gradable Adjectives»

# Who cites «SIGA: A Naturalistic NLI Dataset of English Scalar Implicatures with Gradable Adjectives»

6 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/nizamani-2024-siga-naturalistic-nli-dataset-english/)

1. [Language Models Learn Constructional Semantics, Not To Mention Syntax: Investigating LM Understanding of Paired-Focus Constructions](https://aclanthology.org/2026.conll-main.15/) *— 2026 · Proceedings of the 30th Conference on Computational Natural Language Learning*
2. [Linking language model predictions to human behaviour on scalar implicatures](https://aclanthology.org/2025.neusymbridge-1.10/) *— 2025 · Proceedings of Bridging Neurons and Symbols for Natural Language Processing and Knowledge Graphs Reasoning @ COLING 2025*
3. [Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges](https://aclanthology.org/2025.acl-long.425/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
4. han-2026-crossing-cross-scale-reasoning-evaluation
5. somayajula-2025-improving-language-understanding-capabilities-large
6. staliunaite-2025-dis2dis-explaining-ambiguity-fact-checking
- [← previous Who cites «Incrementality in Deterministic Dependency Parsing» · page 2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/nivre-2004-incrementality-deterministic-dependency-parsing-2/)
- [next → Who cites «Wide-Coverage Efficient Statistical Parsing with CCG and Log-Linear Models»](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/nlpgap-2007-clark-wide-coverage-efficient-statistical-parsing/)
- [↑ back to papers/cited-by/](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/)
