In the SemEval STS competitions, teams create models that need to work well on domains both represented in the training data and hidden domains revealed at test time.
our model and those of Wieting and Gimpel (2018), in contrast to the best performing STS systems, do not use any manually-labeled training examples nor any other linguistic resources beyond the ParaNMT corpus (Wieting and Gimpel, 2018  our fine-tuning objective becomes: We tune γ from the set {0.2, 0.3} in our experiments.