**MT-PET** is a multi-task version of [Pattern Exploiting Training](https://arxiv.org/abs/2001.07676) (PET) for exaggeration detection, which leverages knowledge from complementary cloze-style QA tasks to improve few-shot learning. It defines pairs of complementary pattern-verbalizer pairs for a main task and auxiliary task. These PVPs are then used to train PET on data from both tasks.

PET uses the masked language modeling objective of pretrained language models to transform a task into one or more cloze-style question answering tasks.  In the original PET implementation, PVPs are defined for a single target task. MT-PET extends this by allowing for auxiliary PVPs from related tasks, adding complementary cloze-style QA tasks during training. The motivation for the multi-task approach is two-fold: 1) complementary cloze-style tasks can potentially help the model to learn different aspects of the main task, i.e. the similar tasks of exaggeration detection and claim strength prediction; 2) data on related tasks can be utilized during training, which is important in situations where data for the main task is limited.

**GHM-C** is a loss function designed to balance the gradient flow for anchor classification. The GHM first performs statistics on the number of examples with similar attributes w.r.t their gradient density and then attaches a harmonizing parameter to the gradient of each example according to the density. The modification of gradient can be equivalently implemented by reformulating the loss function. Embedding the GHM into the classification loss is denoted as GHM-C loss. Since the gradient density is a statistical variable depending on the examples distribution in a mini-batch, GHM-C is a dynamic loss that can adapt to the change of data distribution in each batch as well as to the updating of the model.

GHM-C

Gradient Harmonized Single-stage Detector

MT-PET

Semi-Supervised Exaggeration Detection of Health Science Press Releases

**Domain Adaptive Neighborhood Clustering via Entropy Optimization (DANCE)** is a self-supervised clustering method that harnesses the cluster structure of the target domain using self-supervision. This is done with a neighborhood clustering technique that self-supervises feature learning in the target. At the same time, useful source features and class boundaries are preserved and adapted with a partial domain alignment loss that the authors refer to as entropy separation loss. This loss allows the model to either match each target example with the source, or reject it as unknown.

Source	Semi-Supervised Exaggeration Detection of Health Science Press Releases
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com