CGELBank is a treebank and associated tools based on a syntactic formalism
for English derived from the Cambridge Grammar of the English Language. This
document lays out the particularities of the CGELBank annotation scheme.

CGELBank 是基于从《剑桥英语语法》中推导出的一种英语句法形式主义的树库和相关工具，本文概述了 CGELBank 注释方案的特殊性。

CGELBank 注释手册 v1.0

CGELBank Annotation Manual v1.0

Current state-of-the-art neural machine translation (NMT) uses a deep
multi-head self-attention network with no explicit phrase information. However,
prior work on statistical machine translation has shown that extending the
basic translation unit from words to phrases has produced substantial
improvements, suggesting the possibility of improving NMT performance from
explicit modeling of phrases. In this work, we present multi-granularity
self-attention (Mg-Sa): a neural network that combines multi-head
self-attention and phrase modeling. Specifically, we train several attention
heads to attend to phrases in either n-gram or syntactic formalism. Moreover,
we exploit interactions among phrases to enhance the strength of structure
modeling - a commonly-cited weakness of self-attention. Experimental results on
WMT14 English-to-German and NIST Chinese-to-English translation tasks show the
proposed approach consistently improves performance. Targeted linguistic
analysis reveals that Mg-Sa indeed captures useful phrase information at
various levels of granularities.

本文介绍了一种结合多头自注意力和短语建模的新型神经网络 Mg-Sa，利用 n-gram 或者句法格式训练多个注意头以关注短语，并通过短语间的交互增强结构建模弱点，实验结果表明该方法可以提高 NMT 的性能。