This article explores the adaptive relationship between Encoder Layers and
Decoder Layers using the SOTA model Helsinki-NLP/opus-mt-de-en, which
translates German to English. The specific method involves introducing a
bias-free fully connected layer between the Encoder and Decoder, with different
initializations of the layer's weights, and observing the outcomes of
fine-tuning versus retraining. Four experiments were conducted in total. The
results suggest that directly modifying the pre-trained model structure for
fine-tuning yields suboptimal performance. However, upon observing the outcomes
of the experiments with retraining, this structural adjustment shows
significant potential.

该论文探讨了编码器层和解码器层之间的适应性关系，使用 Helsinki-NLP/opus-mt-de-en 这一最先进模型将德语翻译为英语。具体方法是在编码器和解码器之间引入一个无偏差的全连接层，并对该层的权重进行不同的初始化，观察微调与重新训练的结果。总共进行了四个实验，结果表明直接修改预训练模型结构进行微调效果不佳，然而，观察重新训练实验的结果后，这种结构调整显示出了显著的潜力。

重新思考编码器层与解码器层之间的自适应关系

Rethinking the adaptive relationship between Encoder Layers and Decoder  Layers

Recent sparse detectors with multiple, e.g. six, decoder layers achieve
promising performance but much inference time due to complex heads. Previous
works have explored using dense priors as initialization and built
one-decoder-layer detectors. Although they gain remarkable acceleration, their
performance still lags behind their six-decoder-layer counterparts by a large
margin. In this work, we aim to bridge this performance gap while retaining
fast speed. We find that the architecture discrepancy between dense and sparse
detectors leads to feature conflict, hampering the performance of
one-decoder-layer detectors. Thus we propose Adaptive Sparse Anchor Generator
(ASAG) which predicts dynamic anchors on patches rather than grids in a sparse
way so that it alleviates the feature conflict problem. For each image, ASAG
dynamically selects which feature maps and which locations to predict, forming
a fully adaptive way to generate image-specific anchors. Further, a simple and
effective Query Weighting method eases the training instability from
adaptiveness. Extensive experiments show that our method outperforms
dense-initialized ones and achieves a better speed-accuracy trade-off. The code
is available at https://github.com/iSEE-Laboratory/ASAG.

提出了一种自适应稀疏锚点生成器（ASAG），通过预测动态锚点在补丁中而不是网格中的方式，以缓解稀疏检测器中的特征冲突问题，并使用简单有效的查询加权方法来缓解训练的不稳定性，实验结果表明，该方法在速度和准确性上取得了平衡，并优于基于稠密初始的方法。

通过自适应稀疏锚点生成构建强大的一解码层稀疏检测器

ASAG: Building Strong One-Decoder-Layer Sparse Detectors via Adaptive  Sparse Anchor Generation

Simultaneous machine translation models start generating a target sequence
before they have encoded or read the source sequence. Recent approaches for
this task either apply a fixed policy on a state-of-the art Transformer model,
or a learnable monotonic attention on a weaker recurrent neural network-based
structure. In this paper, we propose a new attention mechanism, Monotonic
Multihead Attention (MMA), which extends the monotonic attention mechanism to
multihead attention. We also introduce two novel and interpretable approaches
for latency control that are specifically designed for multiple attentions
heads. We apply MMA to the simultaneous machine translation task and
demonstrate better latency-quality tradeoffs compared to MILk, the previous
state-of-the-art approach. We also analyze how the latency controls affect the
attention span and we motivate the introduction of our model by analyzing the
effect of the number of decoder layers and heads on quality and latency.

本文提出了一种名为 Monotonic Multihead Attention（MMA）的新型注意力机制，可应用于同时翻译的机器翻译任务，并介绍了两种特定于多头注意力的新颖且可解释的延迟控制方法，与最先进的 Milk 方法相比，MMA 具有更好的延迟 - 质量平衡，同时分析了延迟控制对关注范围的影响，通过分析解码器层数和头数对质量和延迟的影响来证明模型的引入。