Satire detection and sentiment analysis are intensively explored natural
language processing (NLP) tasks that study the identification of the satirical
tone from texts and extracting sentiments in relationship with their targets.
In languages with fewer research resources, an alternative is to produce
artificial examples based on character-level adversarial processes to overcome
dataset size limitations. Such samples are proven to act as a regularization
method, thus improving the robustness of models. In this work, we improve the
well-known NLP models (i.e., Convolutional Neural Networks, Long Short-Term
Memory (LSTM), Bidirectional LSTM, Gated Recurrent Units (GRUs), and
Bidirectional GRUs) with adversarial training and capsule networks. The
fine-tuned models are used for satire detection and sentiment analysis tasks in
the Romanian language. The proposed framework outperforms the existing methods
for the two tasks, achieving up to 99.08% accuracy, thus confirming the
improvements added by the capsule layers and the adversarial training in NLP
approaches.

基于对抗可训练和胶囊网络的调优，提出了一种优化了的自然语言处理模型，可用于罗马尼亚语的讽刺检测和情感分析任务，对已有方法进行了超越，准确率高达 99.08%。

针对罗马尼亚讽刺检测和情感分析的对抗胶囊网络

Adversarial Capsule Networks for Romanian Satire Detection and Sentiment  Analysis

In this work, we introduce a corpus for satire detection in Romanian news. We
gathered 55,608 public news articles from multiple real and satirical news
sources, composing one of the largest corpora for satire detection regardless
of language and the only one for the Romanian language. We provide an official
split of the text samples, such that training news articles belong to different
sources than test news articles, thus ensuring that models do not achieve high
performance simply due to overfitting. We conduct experiments with two
state-of-the-art deep neural models, resulting in a set of strong baselines for
our novel corpus. Our results show that the machine-level accuracy for satire
detection in Romanian is quite low (under 73% on the test set) compared to the
human-level accuracy (87%), leaving enough room for improvement in future
research.

使用两个深层神经模型，在罗马尼亚新闻中检测幽默的机器水平准确率不高，表明未来研究有足够的改进空间。

SaRoCo: 检测罗马尼亚新闻文章中的讽刺

SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles

Satire is a form of humorous critique, but it is sometimes misinterpreted by
readers as legitimate news, which can lead to harmful consequences. We observe
that the images used in satirical news articles often contain absurd or
ridiculous content and that image manipulation is used to create fictional
scenarios. While previous work have studied text-based methods, in this work we
propose a multi-modal approach based on state-of-the-art visiolinguistic model
ViLBERT. To this end, we create a new dataset consisting of images and
headlines of regular and satirical news for the task of satire detection. We
fine-tune ViLBERT on the dataset and train a convolutional neural network that
uses an image forensics technique. Evaluation on the dataset shows that our
proposed multi-modal approach outperforms image-only, text-only, and simple
fusion baselines.

本文研究了关于讽刺新闻的多模态方法，并使用基于 ViLBERT 模型的新数据集对其进行评估，结果表明我们提出的多模态方法优于仅使用图像或文本或简单融合的方法。

使用文本和视觉线索进行讽刺检测的多模态方法

A Multi-Modal Method for Satire Detection using Textual and Visual Cues

The automatic detection of satire vs. regular news is relevant for downstream
applications (for instance, knowledge base population) and to improve the
understanding of linguistic characteristics of satire. Recent approaches build
upon corpora which have been labeled automatically based on article sources. We
hypothesize that this encourages the models to learn characteristics for
different publication sources (e.g., "The Onion" vs. "The Guardian") rather
than characteristics of satire, leading to poor generalization performance to
unseen publication sources. We therefore propose a novel model for satire
detection with an adversarial component to control for the confounding variable
of publication source. On a large novel data set collected from German news
(which we make available to the research community), we observe comparable
satire classification performance and, as desired, a considerable drop in
publication classification performance with adversarial training. Our analysis
shows that the adversarial component is crucial for the model to learn to pay
attention to linguistic properties of satire.

本文介绍了一种新的用于讽刺检测的模型，该模型包括对抗性部分以控制发布源的混杂变量，最终得到了可观的讽刺分类性能和显著下降的发布分类性能。通过分析得出，对抗性组件是该模型关注讽刺语言属性的关键。