Publicaties
2023
Wetenschappelijke publicaties
Du, Y., & Nguyen, D. (2023). Measuring the Instability of Fine-Tuning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 6209-6230). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.342
2022
Wetenschappelijke publicaties
Fang, Q., Nguyen, D., & Oberski, D. L. (2022). Evaluating the construct validity of text embeddings with application to survey questions. EPJ Data Science, 11(1), 1-31. [39]. https://doi.org/10.1140/epjds/s13688-022-00353-7
Wegmann, A., Schraagen, M., & Nguyen, D. (2022). Same Author or Just Same Topic? Towards Content-Independent Style Representations. In Proceedings of the 7th Workshop on Representation Learning for NLP (pp. 249-268). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.repl4nlp-1.26
Bilal, I. M., Wang, B., Tsakalidis, A., Nguyen, D., Procter, R., & Liakata, M. (2022). Template-based Abstractive Microblog Opinion Summarization. Transactions of the Association for Computational Linguistics, 10, 1229-1248. https://doi.org/10.1162/tacl_a_00516
2021
Wetenschappelijke publicaties
Herrewijnen, E., Nguyen, D., Mense, J., & Bex, F. (2021). Machine-annotated Rationales: Faithfully Explaining Text Classification. Paper presented at 35th AAAI Conference on Artificial Intelligence.
Du, Y., Fang, Q., & Nguyen, D. (2021). Assessing the Reliability of Word Embedding Gender Bias Measures. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 10012-10034). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.785
https://dspace.library.uu.nl/bitstream/handle/1874/415787/2021.emnlp_main.785.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/415787/2021.emnlp_main.785.pdf?sequence=1
Robertson, A., Liza, F. F., Nguyen, D., McGillivray, B., & Hale, S. (2021). Semantic journeys: quantifying change in emoji meaning from 2012-2018. In Workshop Proceedings of the 15th International AAAI Conference on Web and Social Media AAAI Press. https://doi.org/10.36190/2021.07
Nguyen, D. (2021). Dialect Variation on Social Media. In M. Zampieri, & P. Nakov (Eds.), Similar Languages, Varieties, and Dialects: A Computational Perspective (pp. 204–218). (Studies in Natural Language Processing). Cambridge University Press. https://doi.org/10.1017/9781108565080.014
Nguyen, D., Rosseel, L., & Grieve, J. (2021). On learning and representing social meaning in NLP: a sociolinguistic perspective. In K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. Hakkani-Tur, I. Beltagy, S. Bethard, R. Cotterell, T. Chakraborty, & Y. Zhou (Eds.), Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 603-612). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.naacl-main.50
Vidgen, B., Nguyen, D., Margetts, H., Rossini, P., & Tromble, R. (2021). Introducing CAD: the Contextual Abuse Dataset. In K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. Hakkani-Tur, I. Beltagy, S. Bethard, R. Cotterell, T. Chakraborty, & Y. Zhou (Eds.), Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 2289-2303). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.naacl-main.182
Röttger, P., Vidgen, B., Nguyen, D., Waseem, Z., Margetts, H., & Pierrehumbert, J. (2021). HateCheck: Functional Tests for Hate Speech Detection Models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (pp. 41-58). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.acl-long.4
Wegmann, A., & Nguyen, D. (2021). Does It Capture STEL? A Modular, Similarity-based Linguistic Style Evaluation Framework. In M-F. Moens, X. Huang, L. Specia, & S. W. Yih (Eds.), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 7109-7130). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.569
Du, Y., Fang, Q., & Nguyen, D. (2021). Assessing the Reliability of Word Embedding Gender Bias Measures. (pp. 1-23). arXiv. https://doi.org/10.48550/arXiv.2109.04732
2020
Wetenschappelijke publicaties
Nguyen, D., Liakata, M., Dedeo, S., Eisenstein, J., Mimno, D., Tromble, R., & Winters, J. (2020). How We Do Things With Words: Analyzing Text as Social and Cultural Data. Frontiers in Artificial Intelligence, 3, [62]. https://doi.org/10.3389/frai.2020.00062
Nguyen, D., & Grieve, J. (2020). Do Word Embeddings Capture Spelling Variation? In D. Scott, N. Bel, & C. Zong (Eds.), Proceedings of the 28th International Conference on Computational Linguistics (pp. 870-881). International Committee on Computational Linguistics. https://doi.org/10.18653/v1/2020.coling-main.75
Peinelt, N., Nguyen, D., & Liakata, M. (2020). tBERT: Topic Models and BERT Joining Forces for Semantic Similarity Detection. In D. Jurafsky, J. Chai, N. Schluter, & J. Tetreault (Eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 7047-7055). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.630
2019
Wetenschappelijke publicaties
Vidgen, B., Harris, A., Nguyen, D., Tromble, R., Hale, S., & Margetts, H. (2019). Challenges and frontiers in abusive content detection. In Proceedings of the Third Workshop on Abusive Language Online (pp. 80-93). Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-3509
Peinelt, N., Liakata, M., & Nguyen, D. (2019). Aiming beyond the Obvious: Identifying Non-Obvious Cases in Semantic Similarity Datasets. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 2792-2798). Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1268
Shoemark, P., Liza, F. F., Nguyen, D., Hale, S., & McGillivray, B. (2019). Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 66-76). Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1007