Sophia Althammer


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Ranger: A Toolkit for Effect-Size Based Multi-Task Evaluation
Mete Sertkan | Sophia Althammer | Sebastian Hofstätter
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

In this paper, we introduce Ranger - a toolkit to facilitate the easy use of effect-size-based meta-analysis for multi-task evaluation in NLP and IR. We observed that our communities often face the challenge of aggregating results over incomparable metrics and scenarios, which makes conclusions and take-away messages less reliable. With Ranger, we aim to address this issue by providing a task-agnostic toolkit that combines the effect of a treatment on multiple tasks into one statistical evaluation, allowing for comparison of metrics and computation of an overall summary effect. Our toolkit produces publication-ready forest plots that enable clear communication of evaluation results over multiple tasks. Our goal with the ready-to-use Ranger toolkit is to promote robust, effect-size-based evaluation and improve evaluation standards in the community. We provide two case studies for common IR and NLP settings to highlight Ranger’s benefits.


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Description-based Label Attention Classifier for Explainable ICD-9 Classification
Malte Feucht | Zhiliang Wu | Sophia Althammer | Volker Tresp
Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)

ICD-9 coding is a relevant clinical billing task, where unstructured texts with information about a patient’s diagnosis and treatments are annotated with multiple ICD-9 codes. Automated ICD-9 coding is an active research field, where CNN- and RNN-based model architectures represent the state-of-the-art approaches. In this work, we propose a description-based label attention classifier to improve the model explainability when dealing with noisy texts like clinical notes.