Search results
Results: 13
Number of items: 13
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Bavaresco, A., Bernardi, R., Bertolazzi, L., Elliott, D., Fernández, R., Gatt, A., Ghaleb, E., Giulianelli, M., Hanna, M., Koller, A., Martins, A. F. T., Mondorf, P., Neplenbroek, V., Pezzelle, S., Plank, B., Schlangen, D., Suglia, A., Surikuchi, A. K., Takmaz, E., & Testoni, A. (2025). LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks. In W. Che, J. Nabende, E. Shutova, & M. T. Pilehvar (Eds.), The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025) : proceedings of the conference: ACL 2025 : July 27-August 1, 2025 (Vol. 2, pp. 238–255). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.acl-short.20 -
Takmaz, E., Pezzelle, S., & Fernández, R. (2024). Describing Images Fast and Slow: Quantifying and Predicting the Variation in Human Signals during Visuo-Linguistic Processes. In Y. Graham, & M. Purver (Eds.), The 18th Conference of the European Chapter of the Association for Computational Linguistics : Proceedings of the Conference: EACL 2024 : March 17-22, 2024 (Vol. 1, pp. 2072-2087). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.eacl-long.126
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Takmaz, E., Brandizzi, N., Giulianelli, M., Pezzelle, S., & Fernández, R. (2023). Speaking the Language of Your Listener: Audience-Aware Adaptation via Plug-and-Play Theory of Mind. In A. Rogers, J. Boyd-Graber, & N. Okazaki (Eds.), Findings of the Association for Computational Linguistics: ACL 2023: July 9-14, 2023 (pp. 4198-4217). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-acl.258
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Takmaz, E. (2022). Team DMG at CMCL 2022 Shared Task: Transformer Adapters for the Multi- and Cross-Lingual Prediction of Human Reading Behavior. In E. Chersoni, N. Hollenstein, C. Jacobs, Y. Oseki, L. Prévot, & E. Santus (Eds.), Workshop on Cognitive Modeling and Computational Linguistics: CMCL 2022 : proceedings of the workshop : May 26, 2022 (pp. 136-144). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.cmcl-1.16
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Takmaz, E., Pezzelle, S., & Fernández, R. (2022). Less Descriptive yet Discriminative: Quantifying the Properties of Multimodal Referring Utterances via CLIP. In E. Chersoni, N. Hollenstein, C. Jacobs, Y. Oseki, L. Prévot, & E. Santus (Eds.), Workshop on Cognitive Modeling and Computational Linguistics: CMCL 2022 : proceedings of the workshop : May 26, 2022 (pp. 36-42). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.cmcl-1.4
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Gönül, G., Takmaz, E., & Hohenberger, A. (2021). Preschool children's use of perceptual-motor knowledge and hierarchical representational skills for tool making. Acta Psychologica, 220, Article 103415. https://doi.org/10.1016/j.actpsy.2021.103415
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Pezzelle, S., Takmaz, E., & Fernández, R. (2021). Word Representation Learning in Multimodal Pre-Trained Transformers: An Intrinsic Evaluation. Transactions of the Association of Computational Linguistics, 9, 1563–1579. https://doi.org/10.1162/tacl_a_00443
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Takmaz, E., Giulianelli, M., Pezzelle, S., Sinclair, A., & Fernández, R. (2020). Refer, Reuse, Reduce: Generating Subsequent References in Visual and Conversational Contexts. In B. Webber, T. Cohn, Y. He, & Y. Liu (Eds.), 2020 Conference on Empirical Methods in Natural Language Processing: EMNLP 2020 : proceedings of the conference : November 16-20, 2020 (pp. 4350-4368). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.353
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Takmaz, E., Pezzelle, S., Beinborn, L., & Fernández, R. (2020). Generating Image Descriptions via Sequential Cross-Modal Alignment Guided by Human Gaze. In B. Webber, T. Cohn, Y. He, & Y. Liu (Eds.), 2020 Conference on Empirical Methods in Natural Language Processing: EMNLP 2020 : proceedings of the conference : November 16-20, 2020 (pp. 4664–4677). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.377
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