Many document-level neural machine translation (NMT) systems have explored
the utility of context-aware architecture, usually requiring an increasing
number of parameters and computational complexity. However, few attention is
paid to the baseline model. In this paper, we research extensively the pros and
cons of the standard transformer in document-level translation, and find that
the auto-regressive property can simultaneously bring both the advantage of the
consistency and the disadvantage of error accumulation. Therefore, we propose a
surprisingly simple long-short term masking self-attention on top of the
standard transformer to both effectively capture the long-range dependence and
reduce the propagation of errors. We examine our approach on the two publicly
available document-level datasets. We can achieve a strong result in BLEU and
capture discourse phenomena.

本研究探索了基于上下文感知框架的神经机器翻译系统，研究发现标准 Transformer 自回归属性可以同时带来一致性和误差积累的优势和劣势，因此提出了一种简单的基于长短时记忆的自注意力机制用于捕捉长距离依赖并减少误差传播，在两个公开数据集上验证可以获得较高 BLEU 得分并捕捉语篇现象。

长短时 Masking Transformer: 一种简单而有效的文档级神经机器翻译基线模型

Long-Short Term Masking Transformer: A Simple but Effective Baseline for  Document-level Neural Machine Translation

The problem of predicting human motion given a sequence of past observations
is at the core of many applications in robotics and computer vision. Current
state-of-the-art formulate this problem as a sequence-to-sequence task, in
which a historical of 3D skeletons feeds a Recurrent Neural Network (RNN) that
predicts future movements, typically in the order of 1 to 2 seconds. However,
one aspect that has been obviated so far, is the fact that human motion is
inherently driven by interactions with objects and/or other humans in the
environment. In this paper, we explore this scenario using a novel
context-aware motion prediction architecture. We use a semantic-graph model
where the nodes parameterize the human and objects in the scene and the edges
their mutual interactions. These interactions are iteratively learned through a
graph attention layer, fed with the past observations, which now include both
object and human body motions. Once this semantic graph is learned, we inject
it to a standard RNN to predict future movements of the human/s and object/s.
We consider two variants of our architecture, either freezing the contextual
interactions in the future of updating them. A thorough evaluation in the
"Whole-Body Human Motion Database" shows that in both cases, our context-aware
networks clearly outperform baselines in which the context information is not
considered.

本文利用语义图模型和图注意力层来建立上下文感知体系结构，旨在正确建模人类行为中的对象和人际互动以提高人类运动的预测准确性。通过在全身人类动作数据库上进行的彻底评估，论文显示出模式的优越性。

上下文感知的人体动作预测

Context-aware Human Motion Prediction

Video prediction models based on convolutional networks, recurrent networks,
and their combinations often result in blurry predictions. We identify an
important contributing factor for imprecise predictions that has not been
studied adequately in the literature: blind spots, i.e., lack of access to all
relevant past information for accurately predicting the future. To address this
issue, we introduce a fully context-aware architecture that captures the entire
available past context for each pixel using Parallel Multi-Dimensional LSTM
units and aggregates it using blending units. Our model outperforms a strong
baseline network of 20 recurrent convolutional layers and yields
state-of-the-art performance for next step prediction on three challenging
real-world video datasets: Human 3.6M, Caltech Pedestrian, and UCF-101.
Moreover, it does so with fewer parameters than several recently proposed
models, and does not rely on deep convolutional networks, multi-scale
architectures, separation of background and foreground modeling, motion flow
learning, or adversarial training. These results highlight that full awareness
of past context is of crucial importance for video prediction.

通过使用并行多维 LSTM 单元和融合单元进行数据聚合处理，提出了一种全面考虑过去背景信息的上下文感知结构，从而提高了视频预测的性能。该模型没有使用深度卷积网络、多尺度结构、分离前景和背景建模、运动流学习或对抗性训练等技术，性能超过了强基线模型并在多个领域展示了最新成果。