Papers by Joakim Edin
Normalized AOPC: Fixing Misleading Faithfulness Metrics for Feature Attributions Explainability (2025.acl-long)
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Joakim Edin, Andreas Geert Motzfeldt, Casper L. Christensen, Tuukka Ruotsalo, Lars Maaløe, Maria Maistro
| Challenge: | Deep neural network predictions are notoriously difficult to interpret due to the difficulty in understanding their inner mechanisms. |
| Approach: | They propose to normalize AOPC to enable consistent cross-model evaluations and more meaningful interpretation of individual scores. |
| Outcome: | The proposed approach can radically change AOPC results, questioning the conclusions of earlier studies and offering a more robust framework for assessing feature attribution faithfulness. |
An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records (2024.emnlp-main)
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| Challenge: | State-of-the-art explainability methods rely on human annotations, which are costly. |
| Approach: | They propose an approach to produce plausible and faithful explanations without annotations . they use adversarial robustness training to improve plausibility and AttInGrad . |
| Outcome: | The proposed method produces plausible explanations without human annotations on a medical coding task. |
MultiQT: Multimodal learning for real-time question tracking in speech (2020.acl-main)
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Jakob D. Havtorn, Jan Latko, Joakim Edin, Lars Maaløe, Lasse Borgholt, Lorenzo Belgrano, Nicolai Jacobsen, Regitze Sdun, Željko Agić
| Challenge: | a novel multimodal approach to real-time sequence labeling in speech is proposed . the model treats speech and its own textual representation as two separate modalities . |
| Approach: | They propose a multimodal approach to real-time sequence labeling in speech . they use audio and transcription to jointly learn from a phone call . results show similar pattern of improvements with multimodal learning . |
| Outcome: | The proposed model shows significant gains under adverse noise and limited training data compared to text or audio only under adverse conditions and generalizes to medical symptoms detection. |
Code Like Humans: A Multi-Agent Solution for Medical Coding (2025.findings-emnlp)
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Andreas Geert Motzfeldt, Joakim Edin, Casper L. Christensen, Christian Hardmeier, Lars Maaløe, Anna Rogers
| Challenge: | In medical coding, experts map unstructured clinical notes to alphanumeric codes for diagnoses and procedures. |
| Approach: | They introduce ‘Code Like Humans’: a new agentic framework for medical coding with large language models that implements official coding guidelines for human experts. |
| Outcome: | The proposed framework implements official coding guidelines for human experts and can support the full ICD-10 coding system (+70K labels). |