Mixup is an effective data augmentation method that generates new augmented
samples by aggregating linear combinations of different original samples.
However, if there are noises or aberrant features in the original samples,
Mixup may propagate them to the augmented samples, leading to over-sensitivity
of the model to these outliers . To solve this problem, this paper proposes a
new Mixup method called AMPLIFY. This method uses the Attention mechanism of
Transformer itself to reduce the influence of noises and aberrant values in the
original samples on the prediction results, without increasing additional
trainable parameters, and the computational cost is very low, thereby avoiding
the problem of high resource consumption in common Mixup methods such as
Sentence Mixup . The experimental results show that, under a smaller
computational resource cost, AMPLIFY outperforms other Mixup methods in text
classification tasks on 7 benchmark datasets, providing new ideas and new ways
to further improve the performance of pre-trained models based on the Attention
mechanism, such as BERT, ALBERT, RoBERTa, and GPT. Our code can be obtained at
this https URL

混合是一种有效的数据增强方法，通过聚合不同原始样本的线性组合来生成新的增强样本，本文提出了一种名为 AMPLIFY 的新的混合方法，使用 Transformer 自身的注意机制来降低原始样本中噪音和异常值对预测结果的影响，避免了传统混合方法中资源消耗过高的问题，在 7 个基准数据集上提升了文本分类任务的性能，为基于注意机制的预训练模型如 BERT、ALBERT、RoBERTa 和 GPT 等的性能改进提供了新思路和新方法。

增强：基于注意力的 Mixup 用于 Transformer 的性能提升和标签平滑

AMPLIFY:Attention-based Mixup for Performance Improvement and Label  Smoothing in Transformer

For Japanese-to-English translation, zero pronouns in Japanese pose a
challenge, since the model needs to infer and produce the corresponding pronoun
in the target side of the English sentence. However, although fully resolving
zero pronouns often needs discourse context, in some cases, the local context
within a sentence gives clues to the inference of the zero pronoun. In this
study, we propose a data augmentation method that provides additional training
signals for the translation model to learn correlations between local context
and zero pronouns. We show that the proposed method significantly improves the
accuracy of zero pronoun translation with machine translation experiments in
the conversational domain.

本研究针对日英翻译中的零代词问题，提出了一种数据扩充方法来增强模型对局部文本和零代词之间的关联，实验证明该方法显著提高了在对话领域中零代词翻译的准确性。

日英翻译的零代词数据增广

Zero-pronoun Data Augmentation for Japanese-to-English Translation

We present two novel techniques for detecting zero-velocity events to improve
foot-mounted inertial navigation. Our first technique augments a classical
zero-velocity detector by incorporating a motion classifier that adaptively
updates the detector's threshold parameter. Our second technique uses a long
short-term memory (LSTM) recurrent neural network to classify zero-velocity
events from raw inertial data, in contrast to the majority of zero-velocity
detection methods that rely on basic statistical hypothesis testing. We
demonstrate that both of our proposed detectors achieve higher accuracies than
existing detectors for trajectories including walking, running, and
stair-climbing motions. Additionally, we present a straightforward data
augmentation method that is able to extend the LSTM-based model to different
inertial sensors without the need to collect new training data.

本文提出了两种新的技术来检测零速度事件，以改善足部惯性导航，第一种技术通过引入一个自适应地更新检测器阈值参数的运动分类器扩展了传统零速度检测器，第二种技术使用了一种长时间短期记忆 (LSTM) 递归神经网络来从原始惯性数据中分类零速度事件，并且我们证明了我们提出的两种检测器在包括步行、奔跑和爬楼梯运动的轨迹中都能获得比现有检测器更高的精度。此外，本文还提出了一种简单的数据增强方法，可以扩展 LSTM 模型到不同的惯性传感器，并且不需要收集新的训练数据。