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.

本文分析了几种最近的无监督组分句法分析模型，介绍了强大的基准线，并提出了一种基于少量标注数据的组分句法分析方法，这种方法可以通过简单的数据增强和自我训练进一步提高性能。这表明，为了得出公正的结论，我们应当仔细考虑用于模型开发的标注数据量。