Publication
An Interactive Web-Interface for Visualizing the Inner Workings of the Question Answering LSTM
Ekaterina Loginova; Günter Neumann
In: EMNLP-2018, System Demonstration. Conference on Empirical Methods in Natural Language Processing (EMNLP-2018), November 2-4, Brüssel, Belgium, EMNLP, 11/2018.
Abstract
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.