For over thirty years, researchers have developed and analyzed methods for
latent tree induction as an approach for unsupervised syntactic parsing.
Nonetheless, modern systems still do not perform well enough compared to their
supervised counterparts to have any practical use as structural annotation of
text. In this work, we present a technique that uses distant supervision in the
form of span constraints (i.e. phrase bracketing) to improve performance in
unsupervised constituency parsing. Using a relatively small number of span
constraints we can substantially improve the output from DIORA, an already
competitive unsupervised parsing system. Compared with full parse tree
annotation, span constraints can be acquired with minimal effort, such as with
a lexicon derived from Wikipedia, to find exact text matches. Our experiments
show span constraints based on entities improves constituency parsing on
English WSJ Penn Treebank by more than 5 F1. Furthermore, our method extends to
any domain where span constraints are easily attainable, and as a case study we
demonstrate its effectiveness by parsing biomedical text from the CRAFT
dataset.

本研究提出了一种使用远程监督形式的跨度约束技术来提高无监督语法分析性能的方法。通过少量跨度约束，可以大大提高无监督分析系统 DIORA 的性能。我们的实验表明，基于实体的跨度约束可以使英语 WSJ Penn Treebank 的组成分析提高超过 5 F1，并将其扩展到任何易于实现跨度约束的领域，同时在生物医学文本方面，该方法的有效性得到了证明。

通过跨度约束，在远程监督下改进潜在树归纳

Improved Latent Tree Induction with Distant Supervision via Span  Constraints

Incorporating syntax into neural approaches in NLP has a multitude of
practical and scientific benefits. For instance, a language model that is
syntax-aware is likely to be able to produce better samples; even a
discriminative model like BERT with a syntax module could be used for core NLP
tasks like unsupervised syntactic parsing. Rapid progress in recent years was
arguably spurred on by the empirical success of the Parsing-Reading-Predict
architecture of (Shen et al., 2018a), later simplified by the Order Neuron LSTM
of (Shen et al., 2019). Most notably, this is the first time neural approaches
were able to successfully perform unsupervised syntactic parsing (evaluated by
various metrics like F-1 score).
However, even heuristic (much less fully mathematical) understanding of why
and when these architectures work is lagging severely behind. In this work, we
answer representational questions raised by the architectures in (Shen et al.,
2018a, 2019), as well as some transition-based syntax-aware language models
(Dyer et al., 2016): what kind of syntactic structure can current neural
approaches to syntax represent? Concretely, we ground this question in the
sandbox of probabilistic context-free-grammars (PCFGs), and identify a key
aspect of the representational power of these approaches: the amount and
directionality of context that the predictor has access to when forced to make
parsing decision. We show that with limited context (either bounded, or
unidirectional), there are PCFGs, for which these approaches cannot represent
the max-likelihood parse; conversely, if the context is unlimited, they can
represent the max-likelihood parse of any PCFG.

本文探讨了当前神经语法学习方法对句法结构的表征方式，以及限制神经语法学习方法的表征能力的主要因素，并利用概率上下文无关文法为基础，说明上下文对神经语法学习方法进行语法分析决策时的关键作用。