We study the effect of rich supertag features in greedy transition-based
dependency parsing. While previous studies have shown that sparse boolean
features representing the 1-best supertag of a word can improve parsing
accuracy, we show that we can get further improvements by adding a continuous
vector representation of the entire supertag distribution for a word. In this
way, we achieve the best results for greedy transition-based parsing with
supertag features with $88.6\%$ LAS and $90.9\%$ UASon the English Penn
Treebank converted to Stanford Dependencies.

本研究探讨了在贪婪的基于转移的依存解析中使用丰富的超级标记特征的影响。通过为单词添加 Continue Vector representation of the entire supertag distribution 已经取得了最佳结果，通过在英语 Penn Treebank 上对 Stanford Dependencies 进行转化，最终实现了具有 88.6％ LAS 和 90.9％ UAS 的超级标记功能的贪婪基于转移的解析。

基于贪心的离散和连续超标签特征的依存句法分析

Greedy Transition-Based Dependency Parsing with Discrete and Continuous  Supertag Features

We first observe a potential weakness of continuous vector representations of
symbols in neural machine translation. That is, the continuous vector
representation, or a word embedding vector, of a symbol encodes multiple
dimensions of similarity, equivalent to encoding more than one meaning of the
word. This has the consequence that the encoder and decoder recurrent networks
in neural machine translation need to spend substantial amount of their
capacity in disambiguating source and target words based on the context which
is defined by a source sentence. Based on this observation, in this paper we
propose to contextualize the word embedding vectors using a nonlinear
bag-of-words representation of the source sentence. Additionally, we propose to
represent special tokens (such as numbers, proper nouns and acronyms) with
typed symbols to facilitate translating those words that are not well-suited to
be translated via continuous vectors. The experiments on En-Fr and En-De reveal
that the proposed approaches of contextualization and symbolization improves
the translation quality of neural machine translation systems significantly.

本文首次观察了神经机器翻译中不断向量表示的潜在弱点，基于此提出了利用非线性词袋表示法和类型化符号来提高对源语言和目标语言的区分度，从而显著提高了神经机器翻译系统的翻译质量。