**Pattern-Exploiting Training** is a semi-supervised training procedure that reformulates input examples as cloze-style phrases to help language models understand a given task. These phrases are then used to assign soft labels to a large set of unlabeled examples. Finally, standard supervised training is performed on the resulting training set. 

In the case of PET for sentiment classification, first a number of patterns encoding some form of task description are created to convert training examples to cloze questions; for each pattern, a pretrained language model is finetuned. Secondly, the ensemble of trained models annotates unlabeled data. Lastly, a classifier is trained on the resulting soft-labeled dataset.

A **Double Deep Q-Network**, or **Double DQN** utilises [Double Q-learning](https://paperswithcode.com/method/double-q-learning) to reduce overestimation by decomposing the max operation in the target into action selection and action evaluation. We evaluate the greedy policy according to the online network, but we use the target network to estimate its value.  The update is the same as for [DQN](https://paperswithcode.com/method/dqn), but replacing the target $Y^{DQN}\_{t}$ with:

$$ Y^{DoubleDQN}\_{t} = R\_{t+1}+\gamma{Q}\left(S\_{t+1}, \arg\max\_{a}Q\left(S\_{t+1}, a; \theta\_{t}\right);\theta\_{t}^{-}\right) $$

Compared to the original formulation of Double [Q-Learning](https://paperswithcode.com/method/q-learning), in Double DQN the weights of the second network $\theta^{'}\_{t}$ are replaced with the weights of the target network $\theta\_{t}^{-}$ for the evaluation of the current greedy policy.

Double DQN

Deep Reinforcement Learning with Double Q-learning

Pattern-Exploiting Training

Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference

**Switchable Atrous Convolution (SAC)** softly switches the convolutional computation between different atrous rates and gathers the results using switch functions. The switch functions are spatially dependent, i.e., each location of the feature map might have different switches to control the outputs of SAC. To use SAC in a detector, we convert all the standard 3x3 convolutional layers in the bottom-up backbone to SAC.

Source	Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com