Beinborn, Lisa, and Nora Hollenstein. 2024. Cognitive Plausibility in Natural Language Processing. Springer. https://link.springer.com/book/10.1007/978-3-031-43260-6.

Goriely, Zebulon, Richard Diehl Martinez, Andrew Caines, Lisa Beinborn, and Paula Buttery. 2024. “From Babble to Words:Pre-Training Language Models on Continuous Streams of Phonemes.” https://arxiv.org/abs/2410.11462.

Martinez, Richard Diehl, Zebulon Goriely, Andrew Caines, Paula Buttery, and Lisa Beinborn. 2024. “Mitigating Frequency Bias and Anisotropy in Language Model Pre-Training with Syntactic Smoothing.” https://arxiv.org/abs/2410.11462.

Kamp, Jonathan, Lisa Beinborn, and Antske Fokkens. 2024. “The Role of Syntactic Span Preferences in Post-Hoc Explanation Disagreement.” In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), edited by Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, and Nianwen Xue, 16066–78. Torino, Italia: ELRA and ICCL. https://aclanthology.org/2024.lrec-main.1397.

Kamp, Jonathan, Lisa Beinborn, and Antske Fokkens. 2023. “Dynamic Top-k Estimation Consolidates Disagreement between Feature Attribution Methods.” In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, edited by Houda Bouamor, Juan Pino, and Kalika Bali, 6190–97. Singapore: Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.379.

Martinez, Richard Diehl, Hope McGovern, Zebulon Goriely, Christopher Davis, Andrew Caines, Paula Buttery, and Lisa Beinborn. 2023. “CLIMB – Curriculum Learning for Infant-Inspired Model Building.” In Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning, edited by Alex Warstadt, Aaron Mueller, Leshem Choshen, Ethan Wilcox, Chengxu Zhuang, Juan Ciro, Rafael Mosquera, et al., 112–27. Singapore: Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.conll-babylm.10.

Hobo, Eliza, Charlotte Pouw, and Lisa Beinborn. 2023. “‘Geen Makkie’: Interpretable Classification and Simplification of Dutch Text Complexity.” In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023), edited by Ekaterina Kochmar, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Nitin Madnani, Anaïs Tack, Victoria Yaneva, Zheng Yuan, and Torsten Zesch, 503–17. Toronto, Canada: Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.bea-1.42.

Beinborn, Lisa, Koustava Goswami, Saliha Muradoğlu, Alexey Sorokin, Ritesh Kumar, Andreas Shcherbakov, Edoardo M. Ponti, Ryan Cotterell, and Ekaterina Vylomova, eds. 2023. Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP. Dubrovnik, Croatia: Association for Computational Linguistics. https://aclanthology.org/2023.sigtyp-1.0.

Pouw, Charlotte, Nora Hollenstein, and Lisa Beinborn. 2023. “Cross-Lingual Transfer of Cognitive Processing Complexity.” In Findings of the Association for Computational Linguistics: EACL 2023, edited by Andreas Vlachos and Isabelle Augenstein, 655–69. Dubrovnik, Croatia: Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-eacl.49.

Tufa, Wondimagegnhue, Lisa Beinborn, and Piek Vossen. 2023. “A WordNet View on Crosslingual Transformers.” In Proceedings of the 12th Global Wordnet Conference, edited by German Rigau, Francis Bond, and Alexandre Rademaker, 14–24. University of the Basque Country, Donostia - San Sebastian, Basque Country: Global Wordnet Association. https://aclanthology.org/2023.gwc-1.2.

Fekete, Marcell Richard, Johannes Bjerva, and Lisa Beinborn. 2023. Typological Challenges for the Application of Multilingual Language Models in the Digital Humanities.“ In Multilingual Digital Humanities, 145–64. Routledge.

Hollenstein, Nora, Itziar Gonzalez-Dios, Lisa Beinborn, and Lena Jäger. 2022. “Patterns of Text Readability in Human and Predicted Eye Movements.” In Proceedings of the Workshop on Cognitive Aspects of the Lexicon, edited by Michael Zock, Emmanuele Chersoni, Yu-Yin Hsu, and Enrico Santus, 1–15. Taipei, Taiwan: Association for Computational Linguistics. https://aclanthology.org/2022.cogalex-1.1.

Kamp, Jonathan, Lisa Beinborn, and Antske Fokkens. 2022. “Perturbations and Subpopulations for Testing Robustness in Token-Based Argument Unit Recognition.” In Proceedings of the 9th Workshop on Argument Mining, edited by Gabriella Lapesa, Jodi Schneider, Yohan Jo, and Sougata Saha, 62–73. Online and in Gyeongju, Republic of Korea: International Conference on Computational Linguistics. https://aclanthology.org/2022.argmining-1.5.

Morger, Felix, Stephanie Brandl, Lisa Beinborn, and Nora Hollenstein. 2022. “A Cross-Lingual Comparison of Human and Model Relative Word Importance.” In Proceedings of the 2022 CLASP Conference on (Dis)Embodiment, edited by Simon Dobnik, Julian Grove, and Asad Sayeed, 11–23. Gothenburg, Sweden: Association for Computational Linguistics. https://aclanthology.org/2022.clasp-1.2.

Hassan, Fadi, Wondimagegnhue Tufa, Guillem Collell, Piek Vossen, Lisa Beinborn, Adrian Flanagan, and Kuan Eeik Tan. 2022. “SeqL at SemEval-2022 Task 11: An Ensemble of Transformer Based Models for Complex Named Entity Recognition Task.” In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), edited by Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, and Shyam Ratan, 1583–92. Seattle, United States: Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.semeval-1.218.

Hollenstein, Nora, and Lisa Beinborn. 2021. “Relative Importance in Sentence Processing.” In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), edited by Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli, 141–50. Online: Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.acl-short.19.

Hollenstein, Nora, Federico Pirovano, Ce Zhang, Lena Jäger, and Lisa Beinborn. 2021. “Multilingual Language Models Predict Human Reading Behavior.” In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, edited by Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou, 106–23. Online: Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.naacl-main.10.

Rama, Taraka, Lisa Beinborn, and Steffen Eger. 2020. “Probing Multilingual BERT for Genetic and Typological Signals.” In Proceedings of the 28th International Conference on Computational Linguistics, edited by Donia Scott, Nuria Bel, and Chengqing Zong, 1214–28. Barcelona, Spain (Online): International Committee on Computational Linguistics. https://doi.org/10.18653/v1/2020.coling-main.105.

Takmaz, Ece, Sandro Pezzelle, Lisa Beinborn, and Raquel Fernández. 2020. “Generating Image Descriptions via Sequential Cross-Modal Alignment Guided by Human Gaze.” In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), edited by Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu, 4664–77. Online: Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.377.

Beinborn, Lisa, and Rochelle Choenni. 2020. “Semantic Drift in Multilingual Representations.” Computational Linguistics 46 (3): 571–603. https://doi.org/10.1162/coli_a_00382.

Hollenstein, Nora, Maria Barrett, and Lisa Beinborn. 2020. “Towards Best Practices for Leveraging Human Language Processing Signals for Natural Language Processing.” In Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources, edited by Emmanuele Chersoni, Barry Devereux, and Chu-Ren Huang, 15–27. Marseille, France: European Language Resources Association. https://aclanthology.org/2020.lincr-1.3.

Hendrikx, Eva, and Lisa Beinborn. 2020. “The Fluidity of Concept Representations in Human Brain Signals.” CoRR abs/2002.08880. https://arxiv.org/abs/2002.08880.

Abnar, Samira, Lisa Beinborn, Rochelle Choenni, and Willem Zuidema. 2019. “Blackbox Meets Blackbox: Representational Similarity & Stability Analysis of Neural Language Models and Brains.” In Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, edited by Tal Linzen, Grzegorz Chrupała, Yonatan Belinkov, and Dieuwke Hupkes, 191–203. Florence, Italy: Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-4820.

Takmaz, Ece, Lisa Beinborn, Sandro Pezzelle, and Raquel Fernandez. 2019. “Enhancing Neural Image Captioning with Eye-Tracking.” In Abstract for the EurNLP Summit.

Beinborn, Lisa, Teresa Botschen, and Iryna Gurevych. 2018. “Multimodal Grounding for Language Processing.” In Proceedings of the 27th International Conference on Computational Linguistics, edited by Emily M. Bender, Leon Derczynski, and Pierre Isabelle, 2325–39. Santa Fe, New Mexico, USA: Association for Computational Linguistics. https://aclanthology.org/C18-1197.

Repplinger, Michael, Lisa Beinborn, and Willem Zuidema. 2018. Vector-Space Models of Words and Sentences. Nieuw Archief Voor de Wiskunde, 167–74.

Santos, Pedro Bispo, Lisa Beinborn, and Iryna Gurevych. 2016. “A Domain-Agnostic Approach for Opinion Prediction on Speech.” In Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES), edited by Malvina Nissim, Viviana Patti, and Barbara Plank, 163–72. Osaka, Japan: The COLING 2016 Organizing Committee. https://aclanthology.org/W16-4318.

Beinborn, Lisa, Torsten Zesch, and Iryna Gurevych. 2016. “Predicting the Spelling Difficulty of Words for Language Learners.” In Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications, edited by Joel Tetreault, Jill Burstein, Claudia Leacock, and Helen Yannakoudakis, 73–83. San Diego, CA: Association for Computational Linguistics. https://doi.org/10.18653/v1/W16-0508.

Beinborn, Lisa, Torsten Zesch, and Iryna Gurevych. 2015a. “Candidate Evaluation Strategies for Improved Difficulty Prediction of Language Tests.” In Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications, edited by Joel Tetreault, Jill Burstein, and Claudia Leacock, 1–11. Denver, Colorado: Association for Computational Linguistics. https://doi.org/10.3115/v1/W15-0601.

Beinborn, Lisa, Torsten Zesch, and Iryna Gurevych. 2015b. “Factors of Difficulty in German Language Proficiency Tests.” In Book of Abstracts: Language, Learning, Technology Conference.

Beinborn, Lisa. 2014. “Review: Multilingual Corpora and Multilingual Corpus Analyses.” International Journal of Multilingualism 11 (2): 266–68. https://doi.org/10.1080/14790718.2013.788268.

Beinborn, Lisa, Torsten Zesch, and Iryna Gurevych. 2014. “Predicting the Difficulty of Language Proficiency Tests.” Edited by Dekang Lin, Michael Collins, and Lillian Lee. Transactions of the Association for Computational Linguistics 2:517–30. https://doi.org/10.1162/tacl_a_00200.

Beinborn, Lisa, Torsten Zesch, and Iryna Gurevych. 2013. “Cognate Production Using Character-Based Machine Translation.” In Proceedings of the Sixth International Joint Conference on Natural Language Processing, edited by Ruslan Mitkov and Jong C. Park, 883–91. Nagoya, Japan: Asian Federation of Natural Language Processing. https://aclanthology.org/I13-1112.