Every person speaks or writes their own flavor of their native language,
influenced by a number of factors: the content they tend to talk about, their
gender, their social status, or their geographical origin.
When attempting to perform Machine Translation (MT), these variations have a
significant effect on how the system should perform translation, but this is
not captured well by standard one-size-fits-all models.
In this paper, we propose a simple and parameter-efficient adaptation
technique that only requires adapting the bias of the output softmax to each
particular user of the MT system, either directly or through a factored
approximation.
Experiments on TED talks in three languages demonstrate improvements in
translation accuracy, and better reflection of speaker traits in the target
text.

本文提出了一种简单而参数高效的自适应技术，通过适应输出 softmax 的偏差来适应机器翻译系统的每个特定用户，从而改善翻译准确性和更好地反映说话者特点在目标文本中。

个性化神经机器翻译的极端适应性

Extreme Adaptation for Personalized Neural Machine Translation

In this paper we propose a technique to adapt a convolutional neural network
(CNN) based object counter to additional visual domains and object types while
still preserving the original counting function. Domain-specific normalisation
and scaling operators are trained to allow the model to adjust to the
statistical distributions of the various visual domains. The developed
adaptation technique is used to produce a singular patch-based counting
regressor capable of counting various object types including people, vehicles,
cell nuclei and wildlife. As part of this study a challenging new cell counting
dataset in the context of tissue culture and patient diagnosis is constructed.
This new collection, referred to as the Dublin Cell Counting (DCC) dataset, is
the first of its kind to be made available to the wider computer vision
community. State-of-the-art object counting performance is achieved in both the
Shanghaitech (parts A and B) and Penguins datasets while competitive
performance is observed on the TRANCOS and Modified Bone Marrow (MBM) datasets,
all using a shared counting model.

本文提出了一种调整卷积神经网络目标计数器的技术，在保留原始计数功能的同时，适应于其他视觉领域和对象类型。此项技术成功的实现了细胞计数新数据集 (DCC) 的构建，并取得了多个数据集上的最先进的目标计数性能。