We present CoNTACT: a Dutch language model adapted to the domain of COVID-19
tweets. The model was developed by continuing the pre-training phase of RobBERT
(Delobelle, 2020) by using 2.8M Dutch COVID-19 related tweets posted in 2021.
In order to test the performance of the model and compare it to RobBERT, the
two models were tested on two tasks: (1) binary vaccine hesitancy detection and
(2) detection of arguments for vaccine hesitancy. For both tasks, not only
Twitter but also Facebook data was used to show cross-genre performance. In our
experiments, CoNTACT showed statistically significant gains over RobBERT in all
experiments for task 1. For task 2, we observed substantial improvements in
virtually all classes in all experiments. An error analysis indicated that the
domain adaptation yielded better representations of domain-specific
terminology, causing CoNTACT to make more accurate classification decisions.

利用 2021 年发布的 280 万条荷兰语 COVID-19 相关推文进行预训练的 CoNTACT 模型，相比于 RobBERT，在两项任务中（二元疫苗犹豫检测和检测疫苗犹豫的论据）均表现出显著的性能提升，证明了领域自适应的重要性，并且使得 CoNTACT 在领域特定词汇的表示上更加精准。

CoNTACT: 一种适用于荷兰 COVID-19 的 BERT 模型，用于疫苗犹豫和论证检测

CoNTACT: A Dutch COVID-19 Adapted BERT for Vaccine Hesitancy and Argumentation Detection

Conventional feedback control methods can solve various types of robot
control problems very efficiently by capturing the structure with explicit
models, such as rigid body equations of motion. However, many control problems
in modern manufacturing deal with contacts and friction, which are difficult to
capture with first-order physical modeling. Hence, applying control design
methodologies to these kinds of problems often results in brittle and
inaccurate controllers, which have to be manually tuned for deployment.
Reinforcement learning (RL) methods have been demonstrated to be capable of
learning continuous robot controllers from interactions with the environment,
even for problems that include friction and contacts. In this paper, we study
how we can solve difficult control problems in the real world by decomposing
them into a part that is solved efficiently by conventional feedback control
methods, and the residual which is solved with RL. The final control policy is
a superposition of both control signals. We demonstrate our approach by
training an agent to successfully perform a real-world block assembly task
involving contacts and unstable objects.

本文研究了如何通过将传统反馈控制方法与强化学习相结合，解决包括接触力和摩擦力在内的现代制造业中的机器人控制问题，并通过培训代理来演示我们的方法，成功地执行现实世界的块装配任务。