Another broadly used method it to learn an embedding matrix for each time slice independently; due to the stochastic aspect of word embeddings, the vectorial space for each time slice is different, making them not directly comparable.
authors perform an alignment of the embeddings spaces by optimising a geometric transformation (Hamilton et al., 2016;Dubossarsky et al., 2017;Szymanski, 2017;Kulkarni et al., 2015)).