I work on human-centric and responsible Natural Language Processing (NLP) and Machine Learning (ML) methods for high-stakes applications with a strong focus on healthcare. My team and I propose methods with a focus on the Dutch and European healthcare ecosystems. Some of the research topics I am interested in include NLP and ML methods that are transparent, interpretable, and explainable, that can efficiently unlearn information, that ensure fairness and (patient) privacy, that prevent and mitigate bias, that cope with data scarcity and that generalise across (patient) distributions and (downstream) tasks.
My main research line tackles challenges surrounding clinical NLP, but I also research methods to model the complex interaction between language and other modalities (e.g., images, videos, audio), knowledge graphs, world, and commonsense knowledge.
Other.
I am/have been/will be area chair for ARR 2024 (February), senior PC member for ECAI 2024 and AAAI 2024, co-organiser of the SemEval 2023 Visual Word Sense Disambiguation shared task, area chair for EACL 2021, and co-organiser of the Representation Learning for NLP (RepL4NLP) workshop 2021 (co-located with ACL 2021). I am a faculty member of the European Laboratory for Learning and Intelligent Systems (ELLIS) and a member of the Association for Computational Linguistics (ACL).
References
2024
ViLMA: A Zero-Shot Benchmark for Linguistic and Temporal Grounding in Video-Language Models
Ilker Kesen , Andrea Pedrotti , Mustafa Dogan , Michele Cafagna , Emre Can Acikgoz , Letitia Parcalabescu , Iacer Calixto , Anette Frank , and 3 more authors
In The Twelfth International Conference on Learning Representations , May 2024
@inproceedings{kesen-etal-2024vilma,title={Vi{LMA}: A Zero-Shot Benchmark for Linguistic and Temporal Grounding in Video-Language Models},author={Kesen, Ilker and Pedrotti, Andrea and Dogan, Mustafa and Cafagna, Michele and Acikgoz, Emre Can and Parcalabescu, Letitia and Calixto, Iacer and Frank, Anette and Gatt, Albert and Erdem, Aykut and Erkut, Erdem},booktitle={The Twelfth International Conference on Learning Representations},year={2024},url={https://openreview.net/forum?id=liuqDwmbQJ},}
2023
Video-and-Language (VidL) models and their cognitive relevance
Anne Zonneveld , Albert Gatt , and Iacer Calixto
In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops , Oct 2023
In this paper we give a narrative review of multi-modal video-language (VidL) models. We introduce the current landscape of VidL models and benchmarks, and draw inspiration from neuroscience and cognitive science to propose avenues for future research in VidL models in particular and artificial intelligence (AI) in general. We argue that iterative feedback loops between AI, neuroscience, and cognitive science are essential to spur progress across these disciplines. We motivate why we focus specifically on VidL models and their benchmarks as a promising type of model to bring improvements in AI and categorise current VidL efforts across multiple’cognitive relevance axioms’. Finally, we provide suggestions on how to effectively incorporate this interdisciplinary viewpoint into research on VidL models in particular and AI in general. In doing so, we hope to create awareness of the potential of VidL models to narrow the gap between neuroscience, cognitive science, and AI.
@inproceedings{Zonneveld_2023_ICCV,author={Zonneveld, Anne and Gatt, Albert and Calixto, Iacer},title={Video-and-Language (VidL) models and their cognitive relevance},booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},month=oct,year={2023},pages={325-338},}
Soft-Prompt Tuning to Predict Lung Cancer Using Primary Care Free-Text Dutch Medical Notes
We examine the use of large Transformer-based pretrained language models (PLMs) for the problem of early prediction of lung cancer using free-text patient medical notes of Dutch primary care physicians. Specifically, we investigate: 1) how soft prompt-tuning compares to standard model fine-tuning; 2) whether simpler static word embedding models (WEMs) can be more robust compared to PLMs in highly imbalanced settings; and 3) how models fare when trained on notes from a small number of patients. All our code is available open source in https://bitbucket.org/aumc-kik/prompt_tuning_cancer_prediction/.
@inproceedings{10.1007/978-3-031-34344-5_23,author={Elfrink, Auke and Vagliano, Iacopo and Abu-Hanna, Ameen and Calixto, Iacer},editor={Juarez, Jose M. and Marcos, Mar and Stiglic, Gregor and Tucker, Allan},title={Soft-Prompt Tuning to Predict Lung Cancer Using Primary Care Free-Text Dutch Medical Notes},booktitle={Artificial Intelligence in Medicine},year={2023},publisher={Springer Nature Switzerland},address={Cham},pages={193--198},isbn={978-3-031-34344-5},}
SemEval-2023 Task 1: Visual Word Sense Disambiguation
Alessandro Raganato , Iacer Calixto , Asahi Ushio , Jose Camacho-Collados , and Mohammad Taher Pilehvar
In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023) , Jul 2023
This paper presents the Visual Word Sense Disambiguation (Visual-WSD) task. The objective of Visual-WSD is to identify among a set of ten images the one that corresponds to the intended meaning of a given ambiguous word which is accompanied with minimal context. The task provides datasets for three different languages: English, Italian, and Farsi.We received a total of 96 different submissions. Out of these, 40 systems outperformed a strong zero-shot CLIP-based baseline. Participating systems proposed different zero- and few-shot approaches, often involving generative models and data augmentation. More information can be found on the task’s website: }urlhttps://raganato.github.io/vwsd/.
@inproceedings{raganato-etal-2023-semeval,title={{S}em{E}val-2023 Task 1: Visual Word Sense Disambiguation},author={Raganato, Alessandro and Calixto, Iacer and Ushio, Asahi and Camacho-Collados, Jose and Pilehvar, Mohammad Taher},booktitle={Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)},month=jul,year={2023},address={Toronto, Canada},publisher={Association for Computational Linguistics},url={https://aclanthology.org/2023.semeval-1.308},doi={10.18653/v1/2023.semeval-1.308},pages={2227--2234},}
2022
Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning
Erkut Erdem , Menekse Kuyu , Semih Yagcioglu , Anette Frank , Letitia Parcalabescu , Barbara Plank , Andrii Babii , Oleksii Turuta , and 10 more authors
Developing artificial learning systems that can understand and generate natural language has been one of the long-standing goals of artificial intelligence. Recent decades have witnessed an impressive progress on both of these problems, giving rise to a new family of approaches. Especially, the advances in deep learning over the past couple of years have led to neural approaches to natural language generation (NLG). These methods combine generative language learning techniques with neural-networks based frameworks. With a wide range of applications in natural language processing, neural NLG (NNLG) is a new and fast growing field of research. In this state-of-the-art report, we investigate the recent developments and applications of NNLG in its full extent from a multidimensional view, covering critical perspectives such as multimodality, multilinguality, controllability and learning strategies. We summarize the fundamental building blocks of NNLG approaches from these aspects and provide detailed reviews of commonly used preprocessing steps and basic neural architectures. This report also focuses on the seminal applications of these NNLG models such as machine translation, description generation, automatic speech recognition, abstractive summarization, text simplification, question answering and generation, and dialogue generation. Finally, we conclude with a thorough discussion of the described frameworks by pointing out some open research directions.
@article{10.1613/jair.1.12918,author={Erdem, Erkut and Kuyu, Menekse and Yagcioglu, Semih and Frank, Anette and Parcalabescu, Letitia and Plank, Barbara and Babii, Andrii and Turuta, Oleksii and Erdem, Aykut and Calixto, Iacer and Lloret, Elena and Apostol, Elena-Simona and Truic\u{a}, Ciprian-Octavian and \v{S}andrih, Branislava and Martin\v{c}i\'{c}-Ip\v{s}i\'{c}, Sanda and Berend, G\'{a}bor and Gatt, Albert and Korvel, Gr\u{a}zina},title={Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning},year={2022},issue_date={May 2022},publisher={AI Access Foundation},address={El Segundo, CA, USA},volume={73},issn={1076-9757},url={https://doi.org/10.1613/jair.1.12918},doi={10.1613/jair.1.12918},journal={J. Artif. Int. Res.},month=may,numpages={77},alt_metric={true},dimensions={true},keywords={natural language, neural networks}}
VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena
Letitia Parcalabescu , Michele Cafagna , Lilitta Muradjan , Anette Frank , Iacer Calixto , and Albert Gatt
In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , May 2022
We propose VALSE (Vision And Language Structured Evaluation), a novel benchmark designed for testing general-purpose pretrained vision and language (V&L) models for their visio-linguistic grounding capabilities on specific linguistic phenomena. VALSE offers a suite of six tests covering various linguistic constructs. Solving these requires models to ground linguistic phenomena in the visual modality, allowing more fine-grained evaluations than hitherto possible. We build VALSE using methods that support the construction of valid foils, and report results from evaluating five widely-used V&L models. Our experiments suggest that current models have considerable difficulty addressing most phenomena. Hence, we expect VALSE to serve as an important benchmark to measure future progress of pretrained V&L models from a linguistic perspective, complementing the canonical task-centred V&L evaluations.
@inproceedings{parcalabescu-etal-2022-valse,title={{VALSE}: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena},author={Parcalabescu, Letitia and Cafagna, Michele and Muradjan, Lilitta and Frank, Anette and Calixto, Iacer and Gatt, Albert},booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},month=may,year={2022},address={Dublin, Ireland},publisher={Association for Computational Linguistics},url={https://aclanthology.org/2022.acl-long.567},doi={10.18653/v1/2022.acl-long.567},pages={8253--8280},}
