Irena Spasić

Also published as: Irena Spasic


2024

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CID at RRG24: Attempting in a Conditionally Initiated Decoding of Radiology Report Generation with Clinical Entities
Yuxiang Liao | Yuanbang Liang | Yipeng Qin | Hantao Liu | Irena Spasic
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing

Radiology Report Generation (RRG) seeks to leverage deep learning techniques to automate the reporting process of radiologists. Current methods are typically modelling RRG as an image-to-text generation task that takes X-ray images as input and generates textual reports describing the corresponding clinical observations. However, the wording of the same clinical observation could have been influenced by the expression preference of radiologists. Nevertheless, such variability can be mitigated by normalizing textual reports into structured representations such as a graph structure. In this study, we attempt a novel paradigm for incorporating graph structural data into the RRG model. Our approach involves predicting graph labels based on visual features and subsequently initiating the decoding process through a template injection conditioned on the predicted labels. We trained and evaluated our model on the BioNLP 2024 Shared Task on Large-Scale Radiology Report Generation and submitted our results to the ViLMedic RRG leaderboard. Although our model showed a moderate ranking on the leaderboard, the results provide preliminary evidence for the feasibility of this new paradigm, warranting further exploration and refinement.

2021

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Learning Data Augmentation Schedules for Natural Language Processing
Daphné Chopard | Matthias S. Treder | Irena Spasić
Proceedings of the Second Workshop on Insights from Negative Results in NLP

Despite its proven efficiency in other fields, data augmentation is less popular in the context of natural language processing (NLP) due to its complexity and limited results. A recent study (Longpre et al., 2020) showed for example that task-agnostic data augmentations fail to consistently boost the performance of pretrained transformers even in low data regimes. In this paper, we investigate whether data-driven augmentation scheduling and the integration of a wider set of transformations can lead to improved performance where fixed and limited policies were unsuccessful. Our results suggest that, while this approach can help the training process in some settings, the improvements are unsubstantial. This negative result is meant to help researchers better understand the limitations of data augmentation for NLP.

2019

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Unsupervised multi-word term recognition in Welsh
Irena Spasić | David Owen | Dawn Knight | Andreas Artemiou
Proceedings of the Celtic Language Technology Workshop

2003

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Using Domain-Specific Verbs for Term Classification
Irena Spasic | Goran Nenadic | Sophia Ananiadou
Proceedings of the ACL 2003 Workshop on Natural Language Processing in Biomedicine

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Selecting Text Features for Gene Name Classification: from Documents to Terms
Goran Nenadic | Simon Rice | Irena Spasic | Sophia Ananiadou | Benjamin Stapley
Proceedings of the ACL 2003 Workshop on Natural Language Processing in Biomedicine

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Morpho-syntactic Clues for Terminological Processing in Serbian
Goran Nenadić | Irena Spasić | Sophia Ananiadou
Proceedings of the 2003 EACL Workshop on Morphological Processing of Slavic Languages

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An Integrated Term-Based Corpus Query System
Irena Spasic | Goran Nenadic | Kostas Manios | Sophia Ananiadou
10th Conference of the European Chapter of the Association for Computational Linguistics

2002

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Tuning Context Features with Genetic Algorithms
Irena Spasić | Goran Nenadić | Sophia Ananiadou
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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Automatic Acronym Acquisition and Term Variation Management within Domain-Specific Texts
Goran Nenadić | Irena Spasić | Sophia Ananiadou
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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Automatic Discovery of Term Similarities Using Pattern Mining
Goran Nenadić | Irena Spasić | Sophia Ananiadou
COLING-02: COMPUTERM 2002: Second International Workshop on Computational Terminology