Adding manually annotated prosodic information, specifically pitch accents
and phrasing, to the typical text-based feature set for coreference resolution
has previously been shown to have a positive effect on German data. Practical
applications on spoken language, however, would rely on automatically predicted
prosodic information. In this paper we predict pitch accents (and phrase
boundaries) using a convolutional neural network (CNN) model from acoustic
features extracted from the speech signal. After an assessment of the quality
of these automatic prosodic annotations, we show that they also significantly
improve coreference resolution.

本文介绍了利用卷积神经网络从语音信号的声学特征中提取的音高重音及语调信息对共指消解产生积极影响，并且通过自动预测声调信息的实验结果进一步支持了前人研究的结论。

利用自动预测的韵律信息来改进指代消解

Improving coreference resolution with automatically predicted prosodic  information

Understanding the ways in which information achieves widespread public
awareness is a research question of significant interest. We consider whether,
and how, the way in which the information is phrased --- the choice of words
and sentence structure --- can affect this process. To this end, we develop an
analysis framework and build a corpus of movie quotes, annotated with
memorability information, in which we are able to control for both the speaker
and the setting of the quotes. We find that there are significant differences
between memorable and non-memorable quotes in several key dimensions, even
after controlling for situational and contextual factors. One is lexical
distinctiveness: in aggregate, memorable quotes use less common word choices,
but at the same time are built upon a scaffolding of common syntactic patterns.
Another is that memorable quotes tend to be more general in ways that make them
easy to apply in new contexts --- that is, more portable. We also show how the
concept of "memorable language" can be extended across domains.

探究语言表达是否影响信息在公众中的传播，经过对名言语料的分析，发现使用的词汇和语法句式具有较高的独特性，且更容易被应用到不同的情境中，这种 “易记语言” 的概念可以扩展到不同领域。