R.M.V.K. (Rochelle) Choenni MSc
Faculteit der Natuurwetenschappen, Wiskunde en Informatica
ILLC
Bezoekadres
- Science Park 900
- Kamernummer: L6.42
Postadres
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Postbus 94242
1090 GE Amsterdam
Contactgegevens
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Publicaties
2024
Choenni, R., Shutova, E., & Garrette, D. (2024). Examining Modularity in Multilingual LMs via Language-Specialized Subnetworks. In K. Duh, H. Gomez, & S. Bethard (Eds.), Findings of the Association for Computational Linguistics: NAACL 2024: Findings : June 16-21, 2024 (pp. 287-301). Association for Computational Linguistics. https://aclanthology.org/2024.findings-naacl.21 [details]
2023
- Choenni, R., Garrette, D., & Shutova, E. (2023). Cross-Lingual Transfer with Language-Specific Subnetworks for Low-Resource Dependency Parsing. Computational Linguistics, 49(3), 613-641. https://doi.org/10.1162/coli_a_00482 [details]
- Choenni, R., Garrette, D., & Shutova, E. (2023). How do languages influence each other? Studying cross-lingual data sharing during LM fine-tuning. In H. Bouamor, J. Pino, & K. Bali (Eds.), The 2023 Conference on Empirical Methods in Natural Language Processing: EMNLP 2023 : Proceedings of the Conference : December 6-10, 2023 (pp. 13244-13257). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.818 [details]
- Starace, G., Papakostas, K., Choenni, R., Panagiotopoulos, A., Rosati, M., Leidinger, A., & Shutova, E. (2023). Probing LLMs for Joint Encoding of Linguistic Categories. In H. Bouamor, J. Pino, & K. Bali (Eds.), The 2023 Conference on Empirical Methods in Natural Language Processing : Findings of the Association for Computational Linguistics: EMNLP 2023: December 6-10, 2023 (pp. 7158-7179). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.476 [details]
2022
- Choenni, R., & Shutova, E. (2022). Investigating language relationships in multilingual sentence encoders through the lens of linguistic typology. Computational Linguistics, 48(3), 635–672. https://doi.org/10.1162/coli_a_00444 [details]
2021
- Choenni, R., Shutova, E., & van Rooij, R. (2021). Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you?'. In M-C. Moens, X. Huang, L. Specia, & S. W. Yih (Eds.), 2021 Conference on Empirical Methods in Natural Language Processing: EMNLP 2021 : proceedings of the conference : November 7-11, 2021 (pp. 1477-1491). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.111 [details]
2019
- Abnar, S., Beinborn, L., Choenni, R., & Zuidema, W. (2019). Blackbox Meets Blackbox: Representational Similarity & Stability Analysis of Neural Language Models and Brains. In T. Linzen, G. Chrupała, Y. Belinkov, & D. Hupkes (Eds.), The BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP at ACL 2019: ACL 2019 : proceedings of the Second Workshop : August 1, 2019, Florence, Italy (pp. 191-203). The Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-4820 [details]
- Beinborn, L. M., Abnar, S., & Choenni, R. (in press). Robust Evaluation of Language-Brain Encoding Experiments. International Journal of Computational Linguistics and Applications. https://arxiv.org/abs/1904.02547
2024
- Tong, X., Choenni, R. M. V. K., Lewis, M. A. F., & Shutova, E. V. (2024). Metaphor Understanding Challenge Dataset for LLMs. ArXiv. https://doi.org/10.48550/arXiv.2403.11810
2019
- Choenni, R., Hendrikx, E., & Beinborn, L. M. (2019). On the Evaluation of Structural Similarity between Brain and Computational Models. Poster session presented at Crossing the Boundaries: Language in Interaction, Nijnmegen.
2019
- Beinborn, L. M., & Choenni, R. (2019). Semantic Drift in Multilingual Representations.
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Nevenwerkzaamheden
Geen nevenwerkzaamheden
Choenni, R., Shutova, E., & Garrette, D. (2024). Examining Modularity in Multilingual LMs via Language-Specialized Subnetworks. In K. Duh, H. Gomez, & S. Bethard (Eds.), Findings of the Association for Computational Linguistics: NAACL 2024: Findings : June 16-21, 2024 (pp. 287-301). Association for Computational Linguistics.