Common sources of model instability in modern NLP include weight initialisation, data sub-sampling for stochastic gradient calculation, negative sampling used to train word embeddings (Mikolov et al., 2013) and feature sub-sampling for ensemble methods.
the often state-of-the-art LSTMs (and its many variants) have been shown to exhibit high sensitivity to random seeds (Reimers and Gurevych, 2017).