Papers by Lasse Borgholt

2 papers
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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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.

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