Sina Zarrieß

Sina Zarrieß

sina.zarriess@uni-bielefeld.de
Institution: Bielefeld University
Department: Bielefeld Faculty of Linguistics & Literary Studies
Position: Professor of Computational Linguistics
https://sinazarriess.github.io/

Natural language generation, language & vision, dialogue, computational semantics and pragmatics, computational models of reference in context, visual language grounding at the word, sentence and document level

  • No usage-based linguistics without language use (2026)

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    Published at: Behavioral and Brain Sciences (Volume: 49)
    DOI: 10.1017/S0140525X26104646

  • Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities (2026)

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    Published at: Dialogue and Discourse (Volume: 17)
    DOI: 10.5210/dad.2026.201

  • Surprisal and Metaphor Novelty Judgments: Moderate Correlations and Divergent Scaling Effects Revealed by Corpus-Based and Synthetic Datasets (2026)

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    Published at: Eacl 2026 19th Conference of the European Chapter of the Association for Computational Linguistics Proceedings of the Conference Vol 1 Long Papers (Volume: 1)
    DOI: 10.18653/v1/2026.eacl-long.378

  • SemCSE: Semantic Contrastive Sentence Embeddings Using LLM-Generated Summaries For Scientific Abstracts (2025)

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    Published at: Emnlp 2025 2025 Conference on Empirical Methods in Natural Language Processing Proceedings of the Conference
    DOI: 10.18653/v1/2025.emnlp-main.1662

  • SCENEGRAM: Conceptualizing and Describing Tangrams in Scene Context (2025)

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    Published at: Proceedings of the Annual Meeting of the Association for Computational Linguistics
    DOI: 10.18653/v1/2025.findings-acl.1229

  • Subword models struggle with word learning, but surprisal hides it (2025)

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    Published at: Proceedings of the Annual Meeting of the Association for Computational Linguistics (Volume: 2)
    DOI: 10.18653/v1/2025.acl-short.24

  • Can LLMs Ground when they (Don't) Know: A Study on Direct and Loaded Political Questions (2025)

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    Published at: Proceedings of the Annual Meeting of the Association for Computational Linguistics (Volume: 1)
    DOI: 10.18653/v1/2025.acl-long.728

  • Do Construction Distributions Shape Formal Language Learning in German BabyLMs? (2025)

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    Published at: Conll 2025 29th Conference on Computational Natural Language Learning Proceedings of the Conference
    DOI: 10.18653/v1/2025.conll-1.12

  • Enhancing Domain-Specific Encoder Models with LLM-Generated Data: How to Leverage Ontologies, and How to Do Without Them (2025)

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    Published at: Emnlp 2025 2025 Conference on Empirical Methods in Natural Language Processing Findings of Emnlp 2025
    DOI: 10.18653/v1/2025.findings-emnlp.1238

  • Small Language Models Also Work With Small Vocabularies: Probing the Linguistic Abilities of Grapheme- and Phoneme-Based Baby Llamas (2025)

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    Published at: Proceedings International Conference on Computational Linguistics Coling