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

We study the problem of recognizing video sequences of fingerspelled letters
in American Sign Language (ASL). Fingerspelling comprises a significant but
relatively understudied part of ASL. Recognizing fingerspelling is challenging
for a number of reasons: It involves quick, small motions that are often highly
coarticulated; it exhibits significant variation between signers; and there has
been a dearth of continuous fingerspelling data collected. In this work we
collect and annotate a new data set of continuous fingerspelling videos,
compare several types of recognizers, and explore the problem of signer
variation. Our best-performing models are segmental (semi-Markov) conditional
random fields using deep neural network-based features. In the signer-dependent
setting, our recognizers achieve up to about 92% letter accuracy. The
multi-signer setting is much more challenging, but with neural network
adaptation we achieve up to 83% letter accuracies in this setting.

研究美国手语中指拼字字母的视频序列识别问题，并通过收集和注释新的连续指拼视频数据集，比较多种类型的识别器，探索签名者差异问题。基于深度神经网络的特征，我们的半马尔可夫条件随机场模型在签名者依赖和多签名者情景下均取得较高的字母识别准确率。