  We analyze several recent unsupervised constituency parsing models, which are
tuned with respect to the parsing $F_1$ score on the Wall Street Journal (WSJ)
development set (1,700 sentences). We introduce strong baselines for them, by
training an existing supervised parsing model (Kitaev and Klein, 2018) on the
same labeled examples they access. When training on the 1,700 examples, or even
when using only 50 examples for training and 5 for development, such a few-shot
parsing approach can outperform all the unsupervised parsing methods by a
significant margin. Few-shot parsing can be further improved by a simple data
augmentation method and self-training. This suggests that, in order to arrive
at fair conclusions, we should carefully consider the amount of labeled data
used for model development. We propose two protocols for future work on
unsupervised parsing: (i) use fully unsupervised criteria for hyperparameter
tuning and model selection; (ii) use as few labeled examples as possible for
model development, and compare to few-shot parsing trained on the same labeled
examples.
