  We propose a scheme for self-training of grammaticality models for
constituency analysis based on linguistic tests. A pre-trained language model
is fine-tuned by contrastive estimation of grammatical sentences from a corpus,
and ungrammatical sentences that were perturbed by a syntactic test, a
transformation that is motivated by constituency theory. We show that
consistent gains can be achieved if only certain positive instances are chosen
for training, depending on whether they could be the result of a test
transformation. This way, the positives, and negatives exhibit similar
characteristics, which makes the objective more challenging for the language
model, and also allows for additional markup that indicates the position of the
test application within the sentence.
