dr. Chris van der Lee

Belz, A., Thomson, C., Reiter, E., Abercrombie, G., Alonso-Moral, J. M., Arvan, M., Braggaar, A., Cieliebak, M., Clark, E., van Deemter, K., Dinkar, T., Dušek, O., Eger, S., Fang, Q., Gao, M., Gatt, A., Gkatzia, D., González-Corbelle, J., Hovy, D., ... Yang, D. (2023). Missing Information, Unresponsive Authors, Experimental Flaws: The Impossibility of Assessing the Reproducibility of Previous Human Evaluations in NLP. In S. Tafreshi, A. Akula, J. Sedoc, A. Drozd, A. Rogers, & A. Rumshisky (Eds.), The Fourth Workshop on Insights from Negative Results in NLP (pp. 1-10). Association for Computational Linguistics.

van der Lee, C., Ferreira, T., Emmery, C., Wiltshire, T., & Krahmer, E. (Accepted/In press). Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model. Computational Linguistics, 49(3), 555-611.

van Miltenburg, E., Braggaar, A., Braun, N., Damen, D., Goudbeek, M., van der Lee, C., Tomas, F., & Krahmer, E. (2023). How reproducible is best-worst scaling for human evaluation? A reproduction of `Data-to-text Generation with Macro Planning'. In A. Belz, M. Popović, E. Reiter, C. Thomson, & J. Sedoc (Eds.), Proceedings of the 3rd Workshop on Human Evaluation of NLP Systems (pp. 75-88). Incoma Ltd., Shoumen, Bulgaria. https://aclanthology.org/2023.humeval-1.7

Braggaar, A., Tomas, F., Blomsma, P., Hommes, S., Braun, N., van Miltenburg, E., van der Lee, C., Goudbeek, M., & Krahmer, E. (2022). A reproduction study of methods for evaluating dialogue system output: Replicating Santhanam and Shaikh (2019). In Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges (pp. 86-93). Association for Computational Linguistics. https://aclanthology.org/2022.inlg-genchal.13

van der Lee, C. (2022). Next Steps in Data-to-Text Generation: Towards Better Data, Models, and Evaluation. [Doctoral Thesis, Tilburg University]. Ridderprint.