1 research outputs found
Distilling neural networks into skipgram-level decision lists
Several previous studies on explanation for recurrent neural networks focus
on approaches that find the most important input segments for a network as its
explanations. In that case, the manner in which these input segments combine
with each other to form an explanatory pattern remains unknown. To overcome
this, some previous work tries to find patterns (called rules) in the data that
explain neural outputs. However, their explanations are often insensitive to
model parameters, which limits the scalability of text explanations. To
overcome these limitations, we propose a pipeline to explain RNNs by means of
decision lists (also called rules) over skipgrams. For evaluation of
explanations, we create a synthetic sepsis-identification dataset, as well as
apply our technique on additional clinical and sentiment analysis datasets. We
find that our technique persistently achieves high explanation fidelity and
qualitatively interpretable rules