Automatically generating short summaries from users' online mental health
posts could save counselors' reading time and reduce their fatigue so that they
can provide timely responses to those seeking help for improving their mental
state. Recent Transformers-based summarization models have presented a
promising approach to abstractive summarization. They go beyond sentence
selection and extractive strategies to deal with more complicated tasks such as
novel word generation and sentence paraphrasing. Nonetheless, these models have
a prominent shortcoming; their training strategy is not quite efficient, which
restricts the model's performance. In this paper, we include a curriculum
learning approach to reweigh the training samples, bringing about an efficient
learning procedure. We apply our model on extreme summarization dataset of
MentSum posts -- a dataset of mental health related posts from Reddit social
media. Compared to the state-of-the-art model, our proposed method makes
substantial gains in terms of Rouge and Bertscore evaluation metrics, yielding
3.5% (Rouge-1), 10.4% (Rouge-2), and 4.7% (Rouge-L), 1.5% (Bertscore) relative
improvements.

本研究旨在通过采用课程学习方法，结合基于 Transformers 模型的自动生成摘要技术，从 Reddit 社交媒体上的心理健康相关帖子中提取性问题，从而提高医生的阅读效率和准确性，为人们提供更及时有效的心理卫生服务。相较于目前已有的模型，本方法在 Rouge 和 Bertscore 评估指标上分别有显著的相对改进。

基于课程的思维健康在线帖子抽象摘要

Curriculum-guided Abstractive Summarization for Mental Health Online Posts

Recent advances in Deep Learning have greatly improved performance on various
tasks such as object detection, image segmentation, sentiment analysis. The
focus of most research directions up until very recently has been on beating
state-of-the-art results. This has materialized in the utilization of bigger
and bigger models and techniques which help the training procedure to extract
more predictive power out of a given dataset. While this has lead to great
results, many of which with real-world applications, other relevant aspects of
deep learning have remained neglected and unknown. In this work, we will
present several novel deep learning training techniques which, while capable of
offering significant performance gains they also reveal several interesting
analysis results regarding convergence speed, optimization landscape
smoothness, and adversarial robustness. The methods presented in this work are
the following:
$\bullet$ Perfect Ordering Approximation; a generalized model agnostic
curriculum learning approach. The results show the effectiveness of the
technique for improving training time as well as offer some new insight into
the training process of deep networks.
$\bullet$ Cascading Sum Augmentation; an extension of mixup capable of
utilizing more data points for linear interpolation by leveraging a smoother
optimization landscape. This can be used for computer vision tasks in order to
improve both prediction performance as well as improve passive model
robustness.

本文介绍了几种新颖的深度学习训练技术，如广义模型无关课程学习方法和级联加和增强方法，它们不仅能够提高训练效率并改善深度网络训练过程中的优化平滑度、鲁棒性，而且还能够为计算机视觉任务提供更好的预测性能和模型稳健性。

深度学习训练过程增强

Deep Learning Training Procedure Augmentations

Generating animations from natural language sentences finds its applications
in a a number of domains such as movie script visualization, virtual human
animation and, robot motion planning. These sentences can describe different
kinds of actions, speeds and direction of these actions, and possibly a target
destination. The core modeling challenge in this language-to-pose application
is how to map linguistic concepts to motion animations.
In this paper, we address this multimodal problem by introducing a neural
architecture called Joint Language to Pose (or JL2P), which learns a joint
embedding of language and pose. This joint embedding space is learned
end-to-end using a curriculum learning approach which emphasizes shorter and
easier sequences first before moving to longer and harder ones. We evaluate our
proposed model on a publicly available corpus of 3D pose data and
human-annotated sentences. Both objective metrics and human judgment evaluation
confirm that our proposed approach is able to generate more accurate animations
and are deemed visually more representative by humans than other data driven
approaches.

本文提出了一种名为 JL2P 的神经架构，采用课程学习方法来解决如何将语言概念映射到动作动画的核心建模挑战，并在公开语料库上进行了测试，表明我们的方法能够生成更准确的动画。