Interpretable NLP Models
Deep learning models are always known to be a black box and lacks interpretability compared to traditional machine learning models. So,There is alway a hesitation in adopting deep learning models in user facing applications (especially medical applications). Recent progress in NLP with the advent of Attention based models , LIME and other techniques have helped to solve this. I would like to walkthough each of the techniques and share my experience in deploying explainable models in production.
- Brief introduction on the importance of interpretability
- Introduction to different interpretabilty techniques
2.1 Attention based models
2.3 Extraction based models
2.4 other techniques
- Demo of the techniques.
No specific requirements.
I am Data scientist with a focus on NLP. I have first hand experience of facing problems occuring because of non intrepretability of deep learning models and also I have experience in deploying deep learning based NLP models from protype to production.