  Recurrent Neural Networks (RNNs) have been widely used in Natural Language
Processing (NLP) tasks given its superior performance on processing sequential
data. However, it is challenging to interpret and debug RNNs due to the
inherent complexity and the lack of transparency of RNNs. While many
explainable AI (XAI) techniques have been proposed for RNNs, most of them only
support local explanations rather than global explanations. In this paper, we
present DeepSeer, an interactive system that provides both global and local
explanations of RNN behavior in multiple tightly-coordinated views for model
understanding and debugging. The core of DeepSeer is a state abstraction method
that bundles semantically similar hidden states in an RNN model and abstracts
the model as a finite state machine. Users can explore the global model
behavior by inspecting text patterns associated with each state and the
transitions between states. Users can also dive into individual predictions by
inspecting the state trace and intermediate prediction results of a given
input. A between-subjects user study with 28 participants shows that, compared
with a popular XAI technique, LIME, participants using DeepSeer made a deeper
and more comprehensive assessment of RNN model behavior, identified the root
causes of incorrect predictions more accurately, and came up with more
actionable plans to improve the model performance.
