  In this paper, we study the "stability" of machine learning (ML) models
within the context of larger, complex NLP systems with continuous training data
updates. For this study, we propose a methodology for the assessment of model
stability (which we refer to as jitter under various experimental conditions.
We find that model design choices, including network architecture and input
representation, have a critical impact on stability through experiments on four
text classification tasks and two sequence labeling tasks. In classification
tasks, non-RNN-based models are observed to be more stable than RNN-based ones,
while the encoder-decoder model is less stable in sequence labeling tasks.
Moreover, input representations based on pre-trained fastText embeddings
contribute to more stability than other choices. We also show that two learning
strategies -- ensemble models and incremental training -- have a significant
influence on stability. We recommend ML model designers account for trade-offs
in accuracy and jitter when making modeling choices.
