Papers with interpretability evaluation

2 papers
Explainable Prediction of Medical Codes from Clinical Text (N18-1)

Copied to clipboard

Challenge: Clinical notes are text documents that are created by clinicians for each patient encounter.
Approach: They propose a method that aggregates information across the document using a convolutional neural network and uses an attention mechanism to select the most relevant segments for each of the thousands of possible codes.
Outcome: The proposed method is accurate and better than the current state of the art.
Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability (2021.emnlp-main)

Copied to clipboard

Challenge: Existing models for multi-hop reasoning are not able to evaluate their interpretability . a recent study found that many paths are unreasonable .
Approach: They propose a framework to evaluate the interpretability of multi-hop reasoning models . they annotate all possible rules and establish a benchmark .
Outcome: The proposed framework outperforms existing models in terms of performance and interpretability.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations