This paper investigates the potential of semi-supervised Generative
Adversarial Networks (GANs) to fine-tune pretrained language models in order to
classify Bengali fake reviews from real reviews with a few annotated data. With
the rise of social media and e-commerce, the ability to detect fake or
deceptive reviews is becoming increasingly important in order to protect
consumers from being misled by false information. Any machine learning model
will have trouble identifying a fake review, especially for a low resource
language like Bengali. We have demonstrated that the proposed semi-supervised
GAN-LM architecture (generative adversarial network on top of a pretrained
language model) is a viable solution in classifying Bengali fake reviews as the
experimental results suggest that even with only 1024 annotated samples,
BanglaBERT with semi-supervised GAN (SSGAN) achieved an accuracy of 83.59% and
a f1-score of 84.89% outperforming other pretrained language models -
BanglaBERT generator, Bangla BERT Base and Bangla-Electra by almost 3%, 4% and
10% respectively in terms of accuracy. The experiments were conducted on a
manually labeled food review dataset consisting of total 6014 real and fake
reviews collected from various social media groups. Researchers that are
experiencing difficulty recognizing not just fake reviews but other
classification issues owing to a lack of labeled data may find a solution in
our proposed methodology.

该研究使用半监督生成式对抗网络 (GAN) 探究预训练语言模型微调在少量带有注释数据的情况下，分类孟加拉虚假评论和真实评论的潜力。实验结果表明，即使只有 1024 个标注样本，使用半监督 GAN 可在分类孟加拉虚假评论方面取得 83.59% 的准确率和 84.89% 的 f1 值，优于其他预训练语言模型大约 3%、4% 和 10% 的准确率。该研究提出的方法对于缺乏标记数据的分类问题，尤其是对于像孟加拉语这样的低资源语言，可能会有所帮助。

使用半监督生成对抗网络检测孟加拉虚假评论

Bengali Fake Review Detection using Semi-supervised Generative  Adversarial Networks

In this work, we generalize semi-supervised generative adversarial networks
(GANs) from classification problems to regression problems. In the last few
years, the importance of improving the training of neural networks using
semi-supervised training has been demonstrated for classification problems. We
present a novel loss function, called feature contrasting, resulting in a
discriminator which can distinguish between fake and real data based on feature
statistics. This method avoids potential biases and limitations of alternative
approaches. The generalization of semi-supervised GANs to the regime of
regression problems of opens their use to countless applications as well as
providing an avenue for a deeper understanding of how GANs function. We first
demonstrate the capabilities of semi-supervised regression GANs on a toy
dataset which allows for a detailed understanding of how they operate in
various circumstances. This toy dataset is used to provide a theoretical basis
of the semi-supervised regression GAN. We then apply the semi-supervised
regression GANs to a number of real-world computer vision applications: age
estimation, driving steering angle prediction, and crowd counting from single
images. We perform extensive tests of what accuracy can be achieved with
significantly reduced annotated data. Through the combination of the
theoretical example and real-world scenarios, we demonstrate how
semi-supervised GANs can be generalized to regression problems.

该论文将半监督生成对抗网络从分类问题推广到回归问题，并提出了特征对比的新损失函数，旨在提高神经网络训练的准确率，可以用于计算机视觉等领域的实际应用。