The PDTB-3 contains many more Implicit discourse relations than the previous
PDTB-2. This is in part because implicit relations have now been annotated
within sentences as well as between them. In addition, some now co-occur with
explicit discourse relations, instead of standing on their own. Here we show
that while this can complicate the problem of identifying the location of
implicit discourse relations, it can in turn simplify the problem of
identifying their senses. We present data to support this claim, as well as
methods that can serve as a non-trivial baseline for future state-of-the-art
recognizers for implicit discourse relations.

PDTB-3 增加了更多句子内隐含篇章关系的注释，同时有些隐含关系与显式篇章关系共存，这复杂化了隐含篇章关系的定位问题，但简化了其意义辨析问题，本研究提出了一种基于数据的方法来解决这个问题，这可作为未来隐含篇章关系识别的一种非常规的基准。

将隐含的话语关系识别扩展到 PDTB-3

Extending Implicit Discourse Relation Recognition to the PDTB-3

Images and text co-occur constantly on the web, but explicit links between
images and sentences (or other intra-document textual units) are often not
present. We present algorithms that discover image-sentence relationships
without relying on explicit multimodal annotation in training. We experiment on
seven datasets of varying difficulty, ranging from documents consisting of
groups of images captioned post hoc by crowdworkers to naturally-occurring
user-generated multimodal documents. We find that a structured training
objective based on identifying whether collections of images and sentences
co-occur in documents can suffice to predict links between specific sentences
and specific images within the same document at test time.

通过探索图像和文本之间关联的算法，并基于文档中图像与文本集合是否同时出现的结构化训练目标，在不依赖于显式的多模态注释的情况下发现图像和句子之间的关系。