<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). Furthermore, we generate lattices for standard datasets from a recent WMT Metrics Shared Task and perform the first evaluation of <cite>HyTER</cite> on large and noisier datasets.