Table-to-text generation refers to generating a descriptive text from a
key-value table. Traditional autoregressive methods, though can generate text
with high fluency, suffer from low coverage and poor faithfulness problems. To
mitigate these problems, we propose a novel Skeleton-based two-stage method
that combines both Autoregressive and Non-Autoregressive generations (SANA).
Our approach includes: (1) skeleton generation with an autoregressive pointer
network to select key tokens from the source table; (2) edit-based
non-autoregressive generation model to produce texts via iterative insertion
and deletion operations. By integrating hard constraints from the skeleton, the
non-autoregressive model improves the generation's coverage over the source
table and thus enhances its faithfulness. We conduct automatic and human
evaluations on both WikiPerson and WikiBio datasets. Experimental results
demonstrate that our method outperforms the previous state-of-the-art methods
in both automatic and human evaluation, especially on coverage and
faithfulness. In particular, we achieve PARENT-T recall of 99.47 in WikiPerson,
improving over the existing best results by more than 10 points.

本研究提出了一种基于骨架的两阶段方法，将自回归和非自回归方法相结合用于表格到文本生成中，通过集成骨架的硬约束来提高生成的覆盖率和忠实度，实验结果显示了我们的方法在自动和人工评估中都表现出优越性能。

素描并完善：向忠实和信息丰富的表格到文本生成迈进

Sketch and Refine: Towards Faithful and Informative Table-to-Text  Generation

Human action recognition as an important application of computer vision has
been studied for decades. Among various approaches, skeleton-based methods
recently attract increasing attention due to their robust and superior
performance. However, existing skeleton-based methods ignore the potential
action relationships between different persons, while the action of a person is
highly likely to be impacted by another person especially in complex events. In
this paper, we propose a novel group-skeleton-based human action recognition
method in complex events. This method first utilizes multi-scale
spatial-temporal graph convolutional networks (MS-G3Ds) to extract skeleton
features from multiple persons. In addition to the traditional key point
coordinates, we also input the key point speed values to the networks for
better performance. Then we use multilayer perceptrons (MLPs) to embed the
distance values between the reference person and other persons into the
extracted features. Lastly, all the features are fed into another MS-G3D for
feature fusion and classification. For avoiding class imbalance problems, the
networks are trained with a focal loss. The proposed algorithm is also our
solution for the Large-scale Human-centric Video Analysis in Complex Events
Challenge. Results on the HiEve dataset show that our method can give superior
performance compared to other state-of-the-art methods.

本文介绍了基于多人骨骼的人类动作识别的新方法，利用多尺度时空图卷积网络提取骨骼特征，用多层感知器嵌入参考人和其他人之间的距离值，并通过特征融合和分类得出在 HiEve 数据集上超越其他技术的最佳性能。