AM dependency parsing is a linguistically principled method for neural
semantic parsing with high accuracy across multiple graphbanks. It relies on a
type system that models semantic valency but makes existing parsers slow. We
describe an A* parser and a transition-based parser for AM dependency parsing
which guarantee well-typedness and improve parsing speed by up to 3 orders of
magnitude, while maintaining or improving accuracy.

本论文研究 AM 依赖解析的新方法，包括 A * 和基于转移的解析器，可以提高解析速度并维持或提高准确性。

具有类型保证的快速语义解析

Fast semantic parsing with well-typedness guarantees

Dependency grammar induction is the task of learning dependency syntax
without annotated training data. Traditional graph-based models with global
inference achieve state-of-the-art results on this task but they require
$O(n^3)$ run time. Transition-based models enable faster inference with $O(n)$
time complexity, but their performance still lags behind. In this work, we
propose a neural transition-based parser for dependency grammar induction,
whose inference procedure utilizes rich neural features with $O(n)$ time
complexity. We train the parser with an integration of variational inference,
posterior regularization and variance reduction techniques. The resulting
framework outperforms previous unsupervised transition-based dependency parsers
and achieves performance comparable to graph-based models, both on the English
Penn Treebank and on the Universal Dependency Treebank. In an empirical
comparison, we show that our approach substantially increases parsing speed
over graph-based models.

本研究提出了一种基于神经网络的转移句法解析器，利用变分推断、后验正则化和方差减小技术训练模型，实现了无监督依赖语法归纳并在英语 Penn Treebank 和 Universal Dependency Treebank 上取得了可以与基于图的模型相媲美的表现，并且在实验比较中显示出比基于图的模型更快的解析速度。

神经变分转移句法分析中的依赖语法归纳

Dependency Grammar Induction with a Neural Variational Transition-based  Parser

This paper describes a new statistical parser which is based on probabilities
of dependencies between head-words in the parse tree. Standard bigram
probability estimation techniques are extended to calculate probabilities of
dependencies between pairs of words. Tests using Wall Street Journal data show
that the method performs at least as well as SPATTER (Magerman 95, Jelinek et
al 94), which has the best published results for a statistical parser on this
task. The simplicity of the approach means the model trains on 40,000 sentences
in under 15 minutes. With a beam search strategy parsing speed can be improved
to over 200 sentences a minute with negligible loss in accuracy.

该研究描述了一种基于头词间依赖概率的新型统计解析器，将标准的二元概率估计技术扩展到计算词对间的依赖概率，测试表明该方法至少与 SPATTER（Magerman 95，Jelinek et al 94）相当，模型的简单性意味着其在不到 15 分钟的时间内可以从 40,000 个句子中训练完成；使用 beam search 策略可以提高解析速度到超过 200 个句子 / 分钟，而准确性损失很小。