Papers by Tingting Mu
Evaluation and LLM-Guided Learning of ICD Coding Rationales (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing studies on the explainability of ICD coding rely on attention-based rationales and qualitative assessments conducted by physicians. |
| Approach: | They propose to evaluate the explainability of rationales in ICD coding using a multi-granular rationale-annotated dataset. |
| Outcome: | The proposed model improves the explainability of rationales in ICD coding by using human-annotated rationale-announced rationale models. |
Modelling Instance-Level Annotator Reliability for Natural Language Labelling Tasks (N19-1)
Copied to clipboard
| Challenge: | Existing models that estimate annotators' reliability only consider binary labels and multi-class labels. |
| Approach: | They propose an unsupervised model which can handle binary and multi-class labels and integrate neural networks to model the dependency between latent variables and instances. |
| Outcome: | The proposed model can handle binary and multi-class labels and can estimate reliability of annotators across instances. |