- Yulia Zinova, David Arps, Katharina Spalek, and Jacopo Romoli. 2025. Linking language model predictions to human behaviour on scalar implicatures. In Proceedings of Bridging Neurons and Symbols for Natural Language Processing and Knowledge Graphs Reasoning @ COLING 2025, pages 97–106, Abu Dhabi, UAE. ELRA and ICCL.
- Renato Vukovic, David Arps, Carel van Niekerk, Benjamin Matthias Ruppik, Hsien-chin Lin, Michael Heck, and Milica Gasic. 2024. Dialogue Ontology Relation Extraction via Constrained Chain-of-Thought Decoding. In Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 370–384, Kyoto, Japan. Association for Computational Linguistics.
- David Arps, Laura Kallmeyer, Younes Samih, and Hassan Sajjad. 2024. Multilingual Nonce Dependency Treebanks: Understanding how Language Models Represent and Process Syntactic Structure. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 7822–7844, Mexico City, Mexico. Association for Computational Linguistics
Preliminary work
- Sri Harsha Dumpala, David Arps, Sageev Oore, Laura Kallmeyer, and Hassan Sajjad. 2024. Seeing syntax: Uncovering syntactic learning limitations in vision-language models. Available at https://arxiv.org/abs/2412.08111
- Omar Momen, David Arps, and Laura Kallmeyer. 2023. Increasing The Performance of Cognitively Inspired Data-Efficient Language Models via Implicit Structure Building. In Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning, pages 327–338, Singapore. Association for Computational Linguistics.
- David Arps, Younes Samih, Laura Kallmeyer, and Hassan Sajjad. 2022. Probing for Constituency Structure in Neural Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6738–6757, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.