This paper details our participation in the Challenges and Applications of
Automated Extraction of Socio-political Events from Text (CASE) workshop @
EMNLP 2022, where we take part in Subtask 1 of Shared Task 3. We approach the
given task of event causality detection by proposing a self-training pipeline
that follows a teacher-student classifier method. More specifically, we
initially train a teacher model on the true, original task data, and use that
teacher model to self-label data to be used in the training of a separate
student model for the final task prediction. We test how restricting the number
of positive or negative self-labeled examples in the self-training process
affects classification performance. Our final results show that using
self-training produces a comprehensive performance improvement across all
models and self-labeled training sets tested within the task of event causality
sequence classification. On top of that, we find that self-training performance
did not diminish even when restricting either positive/negative examples used
in training. Our code is be publicly available at
this https URL.

本文介绍了我们参加 EMNLP 2022 的 CASE 研讨会的情况，利用自我训练方法解决了事件因果关系检测的问题，并验证了在不同训练集数量下模型性能的变化，结果表明自我训练可以显著提高模型性能，代码可在此 https 链接中公开获取。

1Cademy @ Causal News Corpus 2022: 在社会政治事件数据的因果分类中利用自训练

1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality Classification of Socio-Political Event Data

Neural painting refers to the procedure of producing a series of strokes for
a given image and non-photo-realistically recreating it using neural networks.
While reinforcement learning (RL) based agents can generate a stroke sequence
step by step for this task, it is not easy to train a stable RL agent. On the
other hand, stroke optimization methods search for a set of stroke parameters
iteratively in a large search space; such low efficiency significantly limits
their prevalence and practicality. Different from previous methods, in this
paper, we formulate the task as a set prediction problem and propose a novel
Transformer-based framework, dubbed Paint Transformer, to predict the
parameters of a stroke set with a feed forward network. This way, our model can
generate a set of strokes in parallel and obtain the final painting of size 512
* 512 in near real time. More importantly, since there is no dataset available
for training the Paint Transformer, we devise a self-training pipeline such
that it can be trained without any off-the-shelf dataset while still achieving
excellent generalization capability. Experiments demonstrate that our method
achieves better painting performance than previous ones with cheaper training
and inference costs. Codes and models are available.

本文提出了一种名为 Paint Transformer 的基于 Transformer 结构的神经网络框架，通过一个前馈网络来预测笔画集合的参数，从而实现在短时间内生成一个 512 * 512 的绘画。此外，作者设计了一个自学习流程，使其可以在没有任何预训练数据的情况下进行训练，并取得了比以往更好的绘画性能。

画笔变换器：带有笔触预测的前馈神经绘画

Paint Transformer: Feed Forward Neural Painting with Stroke Prediction

We present a new domain adaptive self-training pipeline, named ST3D, for
unsupervised domain adaptation on 3D object detection from point clouds. First,
we pre-train the 3D detector on the source domain with our proposed random
object scaling strategy for mitigating the negative effects of source domain
bias. Then, the detector is iteratively improved on the target domain by
alternatively conducting two steps, which are the pseudo label updating with
the developed quality-aware triplet memory bank and the model training with
curriculum data augmentation. These specific designs for 3D object detection
enable the detector to be trained with consistent and high-quality pseudo
labels and to avoid overfitting to the large number of easy examples in pseudo
labeled data. Our ST3D achieves state-of-the-art performance on all evaluated
datasets and even surpasses fully supervised results on KITTI 3D object
detection benchmark. Code will be available at
this https URL

本论文提出了一种新的域自适应自训练流水线（ST3D），用于基于点云的 3D 物体检测的无监督域自适应。该流水线通过特定的设计使检测器能够与一致且高质量的伪标签进行训练，并避免过度配合伪标记数据中大量的易样本，实现了对所有评估数据集的领先性能，并甚至超过了 KITTI 3D 目标检测基准的全监督结果。