My Publications

2024

  • Rohanian, O., Nouriborji, M., Kouchaki, S., Nooralahzadeh, F., Clifton, L., & Clifton, D. A. (2024). Exploring the effectiveness of instruction tuning in biomedical language processing. Artificial Intelligence in Medicine, 158, 103007. https://doi.org/10.1016/j.artmed.2024.103007

  • Taylor, N., Ghose, U., Rohanian, O., Nouriborji, M., Kormilitzin, A., Clifton, D. A., & Nevado-Holgado, A. (2024). Efficiency at scale: Investigating the performance of diminutive language models in clinical tasks. Artificial Intelligence in Medicine, 157, 103002. https://doi.org/10.1016/j.artmed.2024.103002

  • Seminog, O., Furst, R., Mendy, T., Rohanian, O., Levanita, S., Kadri-Alabi, Z., Jabin, N., Humphreys, G., Antonio, E., Bucher, A., & others. (2024). A protocol for a living mapping review of global research funding for infectious diseases with a pandemic potential—Pandemic PACT. Wellcome Open Research, 9, 156.

  • Rohanian, O., Nouriborji, M., Jauncey, H., Kouchaki, S., Nooralahzadeh, F., Clifton, L., Merson, L., Clifton, D. A., & ISARIC Clinical Characterisation Group. (2024). Lightweight transformers for clinical natural language processing. Natural Language Engineering, 30(5), 887–914. https://doi.org/10.1017/S1351324924000134

  • Rohanian, O., Nouriborji, M., Seminog, O., Furst, R., Mendy, T., Levanita, S., Kadri-Alabi, Z., Jabin, N., Toale, D., Humphreys, G., & others. (2024). Rapid biomedical research classification: The Pandemic PACT advanced categorisation engine. arXiv preprint arXiv:2407.10086.

  • Chauhan, V. K., Thakur, A., O’Donoghue, O., Rohanian, O., Molaei, S., & Clifton, D. A. (2024). Continuous patient state attention model for addressing irregularity in electronic health records. BMC Medical Informatics and Decision Making, 24(1), 117. https://doi.org/10.1186/s12911-024-02503-4

  • Liu, F., Li, Z., Zhou, H., Yin, Q., Yang, J., Tang, X., Luo, C., Zeng, M., Jiang, H., Gao, Y., Nigam, P., Nag, S., Yin, B., Hua, Y., Zhou, X., Rohanian, O., Thakur, A., Clifton, L., & Clifton, D. A. (2024). Large language models are poor clinical decision-makers: A comprehensive benchmark. In Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Eds.), Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 13696–13710). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.emnlp-main.759

2023

  • Rohanian, M., Nooralahzadeh, F., Rohanian, O., Clifton, D., & Krauthammer, M. (2023). Disfluent cues for enhanced speech understanding in large language models. In H. Bouamor, J. Pino, & K. Bali (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 3676–3684). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.238

  • Rohanian, O., Nouriborji, M., Kouchaki, S., & Clifton, D. A. (2023). On the effectiveness of compact biomedical transformers. Bioinformatics, 39(3), btad103. https://doi.org/10.1093/bioinformatics/btad103

  • Rohanian, O., Jauncey, H., Nouriborji, M., Kumar, V., Gonçalves, B. P., Kartsonaki, C., ISARIC Clinical Characterisation Group, Merson, L., & Clifton, D. (2023). Using bottleneck adapters to identify cancer in clinical notes under low-resource constraints. In D. Demner-Fushman, S. Ananiadou, & K. Cohen (Eds.), The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks (pp. 62–78). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.bionlp-1.5

  • Nouriborji, M., Rohanian, O., Kouchaki, S., & Clifton, D. A. (2023). MiniALBERT: Model distillation via parameter-efficient recursive transformers. In A. Vlachos & I. Augenstein (Eds.), Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (pp. 1161–1173). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.eacl-main.83

2022

  • Rohanian, O., Kouchaki, S., Soltan, A., Yang, J., Rohanian, M., Yang, Y., & Clifton, D. (2022). Privacy-aware early detection of COVID-19 through adversarial training. IEEE Journal of Biomedical and Health Informatics, 27(3), 1249–1258. https://doi.org/10.1109/JBHI.2022.3227749

  • Nouriborji, M., Rohanian, O., & Clifton, D. (2022). Nowruz at SemEval-2022 Task 7: Tackling cloze tests with transformers and ordinal regression. In G. Emerson, N. Schluter, G. Stanovsky, R. Kumar, A. Palmer, N. Schneider, S. Singh, & S. Ratan (Eds.), Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) (pp. 1071–1077). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.semeval-1.151

  • Soltan, A., Yang, J., Pattanshetty, R., Novak, A., Yang, Y., Rohanian, O., Beer, S., Soltan, M., Thickett, D., Fairhead, R., & others. (2022). Real-world evaluation of AI-driven COVID-19 triage for emergency admissions: External validation & operational assessment of lab-free and high-throughput screening solutions. Lancet Digital Health, 4(4).

2020

  • Rohanian, O., Rei, M., Taslimipoor, S., & Ha, L. A. (2020). Verbal multiword expressions for identification of metaphor. In D. Jurafsky, J. Chai, N. Schluter, & J. Tetreault (Eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 2890–2895). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.259

  • Rohanian, O. (2020). Contributions to the computational treatment of non-literal language [Doctoral dissertation, University of Wolverhampton]. https://wlv.openrepository.com/server/api/core/bitstreams/c8f50e97-e3d2-4c7b-9333-1ce9fe8a9a0a/content

2019

  • Taslimipoor, S., Rohanian, O., & Ha, L. A. (2019). Cross-lingual transfer learning and multitask learning for capturing multiword expressions. In A. Savary, C. P. Escartín, F. Bond, J. Mitrović, & V. B. Mititelu (Eds.), Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019) (pp. 155–161). Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-5119

  • Taslimipoor, S., Rohanian, O., & Može, S. (2019). GCN-Sem at SemEval-2019 Task 1: Semantic parsing using graph convolutional and recurrent neural networks. In J. May, E. Shutova, A. Herbelot, X. Zhu, M. Apidianaki, & S. M. Mohammad (Eds.), Proceedings of the 13th International Workshop on Semantic Evaluation (pp. 102–106). Association for Computational Linguistics. https://doi.org/10.18653/v1/S19-2014

  • Rohanian, O., Taslimipoor, S., Kouchaki, S., Ha, L. A., & Mitkov, R. (2019). Bridging the gap: Attending to discontinuity in identification of multiword expressions. In J. Burstein, C. Doran, & T. Solorio (Eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 2692–2698). Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1275

2018

  • Taslimipoor, S., & Rohanian, O. (2018). Shoma at PARSEME shared task on automatic identification of VMWEs: Neural multiword expression tagging with high generalisation. arXiv preprint arXiv:1809.03056.

  • Taslimipoor, S., Rohanian, O., Ha, L. A., Corpas Pastor, G., & Mitkov, R. (2018). Wolves at SemEval-2018 Task 10: Semantic discrimination based on knowledge and association. In M. Apidianaki, S. M. Mohammad, J. May, E. Shutova, S. Bethard, & M. Carpuat (Eds.), Proceedings of the 12th International Workshop on Semantic Evaluation (pp. 972–976). Association for Computational Linguistics. https://doi.org/10.18653/v1/S18-1160

  • Taslimipoor, S., Rohanian, O., Mitkov, R., & Fazly, A. (2018). Identification of multiword expressions: A fresh look at modelling and evaluation. In Multiword expressions at length and in depth: Extended papers from the MWE 2017 workshop (Vol. 2, p. 299). Language Science Press.

2017

  • Yaneva, V., Orăsan, C., Evans, R., & Rohanian, O. (2017). Combining multiple corpora for readability assessment for people with cognitive disabilities. In J. Tetreault, J. Burstein, C. Leacock, & H. Yannakoudakis (Eds.), Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications (pp. 121–132). Association for Computational Linguistics. https://doi.org/10.18653/v1/W17-5013

  • Rohanian, O., Taslimipoor, S., Yaneva, V., & Ha, L. A. (2017). Using gaze data to predict multiword expressions. In R. Mitkov & G. Angelova (Eds.), Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017 (pp. 601–609). INCOMA Ltd. https://doi.org/10.26615/978-954-452-049-6_078

  • Taslimipoor, S., Rohanian, O., Mitkov, R., & Fazly, A. (2017). Investigating the opacity of verb-noun multiword expression usages in context. In S. Markantonatou, C. Ramisch, A. Savary, & V. Vincze (Eds.), Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017) (pp. 133–138). Association for Computational Linguistics. https://doi.org/10.18653/v1/W17-1718

  • Yaneva, V., Taslimipoor, S., Rohanian, O., & Ha, L. A. (2017). Cognitive processing of multiword expressions in native and non-native speakers of English: Evidence from gaze data. In Computational and Corpus-Based Phraseology: Second International Conference, Europhras 2017, London, UK, November 13-14, 2017, Proceedings 2 (pp. 363–379). Springer.