We present an event structure classification empirically derived from
inferential properties annotated on sentence- and document-level Universal
Decompositional Semantics (UDS) graphs. We induce this classification jointly
with semantic role, entity, and event-event relation classifications using a
document-level generative model structured by these graphs. To support this
induction, we augment existing annotations found in the UDS1.0 dataset, which
covers the entirety of the English Web Treebank, with an array of inferential
properties capturing fine-grained aspects of the temporal and aspectual
structure of events. The resulting dataset (available at decomp.io) is the
largest annotation of event structure and (partial) event coreference to date.

本论文基于句子 - 和文档级别的通用分解语义（UDS）图上的推论属性，以及支持该归纳的领域内现有注释，提出一个与语义角色、实体和事件 - 事件关系分类相结合的事件结构分类。通过使用这些图结构化的文档级生成模型，这篇论文识别了包括语境中细粒度事件的时间和语法结构的大量推论属性，从而构建了目前最大的事件结构和（部分）事件指代注释数据集。

分解和重新组合事件结构

Decomposing and Recomposing Event Structure

We present models for embedding words in the context of surrounding words.
Such models, which we refer to as token embeddings, represent the
characteristics of a word that are specific to a given context, such as word
sense, syntactic category, and semantic role. We explore simple, efficient
token embedding models based on standard neural network architectures. We learn
token embeddings on a large amount of unannotated text and evaluate them as
features for part-of-speech taggers and dependency parsers trained on much
smaller amounts of annotated data. We find that predictors endowed with token
embeddings consistently outperform baseline predictors across a range of
context window and training set sizes.

我们提出了一种基于标准神经网络架构的简单，高效的令牌嵌入模型，并通过大量未注释的文本学习了令牌嵌入，评估它们作为基于少量注释的数据训练的词性标注器和依存分析器的特征，发现词嵌入在各种上下文窗口和训练集大小下都能一致优于基准预测模型。