Log In Sign Up

Model Explainability in Deep Learning Based Natural Language Processing

by   Shafie Gholizadeh, et al.

Machine learning (ML) model explainability has received growing attention, especially in the area related to model risk and regulations. In this paper, we reviewed and compared some popular ML model explainability methodologies, especially those related to Natural Language Processing (NLP) models. We then applied one of the NLP explainability methods Layer-wise Relevance Propagation (LRP) to a NLP classification model. We used the LRP method to derive a relevance score for each word in an instance, which is a local explainability. The relevance scores are then aggregated together to achieve global variable importance of the model. Through the case study, we also demonstrated how to apply the local explainability method to false positive and false negative instances to discover the weakness of a NLP model. These analysis can help us to understand NLP models better and reduce the risk due to the black-box nature of NLP models. We also identified some common issues due to the special natures of NLP models and discussed how explainability analysis can act as a control to detect these issues after the model has been trained.


page 1

page 2

page 3

page 4


Explaining Predictions from Machine Learning Models: Algorithms, Users, and Pedagogy

Model explainability has become an important problem in machine learning...

Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models

Motivations for methods in explainable artificial intelligence (XAI) oft...

NLP Inspired Training Mechanics For Modeling Transient Dynamics

In recent years, Machine learning (ML) techniques developed for Natural ...

Regularization and False Alarms Quantification: Two Sides of the Explainability Coin

Regularization is a well-established technique in machine learning (ML) ...

Towards Prediction Explainability through Sparse Communication

Explainability is a topic of growing importance in NLP. In this work, we...