<cite>HyTER</cite> spurred a lot of enthusiasm but the need for human annotations heavily reduced its applicability to new data. We propose to use an embedding-based lexical substitution model (Melamud et al., 2015) for building this type of reference networks and test, for the first time, the metric with automatically generated lattices (hereafter HyTERA). We show that HyTERA strongly correlates with <cite>HyTER</cite> with hand-crafted lattices, and approximates the hTER score (Snover et al., 2006) as measured using post-edits made by human annotators.