An Interactive Web-Interface for Visualizing the Inner Workings of the Question Answering LSTM
Ekaterina Loginova, Günter Neumann · 2018
Deep learning models for NLP are potent but not readily interpretable.It prevents researchers from improving a model's performance efficiently and users from applying it for a task which requires a high level of trust in the system.We present a visualisation tool which aims to illuminate the inner workings of a specific LSTM model for question answering.It plots heatmaps of neurons' firings and allows a user to check the dependency between neurons and manual features.The system possesses an interactive web-interface and can be adapted to other models and domains.