# Adversarial Capsule Networks for Romanian Satire Detection and Sentiment Analysis

arXiv: https://arxiv.org/abs/2306.07845 · HTML: https://mlsanity.com/paper/2306.07845/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 43 references that are in the map.
- First posted on arXiv 13 Jun 2023.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Adversarial Training Methods for Semi-Supervised Text Classification](https://mlsanity.com/paper/1605.07725/) (2016)
- [Generating Adversarial Examples with Adversarial Networks](https://mlsanity.com/paper/1801.02610/) (2018)
- [Correlation Alignment for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1612.01939/) (2016)
- [The birth of Romanian BERT](https://mlsanity.com/paper/2009.08712/) (2020)
- [Investigating Capsule Networks with Dynamic Routing for Text Classification](https://mlsanity.com/paper/1804.00538/) (2018)
- [Large-Scale Contextualised Language Modelling for Norwegian](https://mlsanity.com/paper/2104.06546/) (2021)
- [MOROCO: The Moldavian and Romanian Dialectal Corpus](https://mlsanity.com/paper/1901.06543/) (2019)
- [Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features](https://mlsanity.com/paper/1709.01189/) (2017)
- [Adversarial Training for Satire Detection: Controlling for Confounding Variables](https://mlsanity.com/paper/1902.11145/) (2019)
- [Distilling the Knowledge of Romanian BERTs Using Multiple Teachers](https://mlsanity.com/paper/2112.12650/) (2021)
- [Introducing RONEC -- the Romanian Named Entity Corpus](https://mlsanity.com/paper/1909.01247/) (2019)
- [SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles](https://mlsanity.com/paper/2105.06456/) (2021)
- [Text Classification using Capsules](https://mlsanity.com/paper/1808.03976/) (2018)
- [Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications](https://mlsanity.com/paper/1906.02829/) (2019)
- [Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data Set](https://mlsanity.com/paper/2101.04197/) (2021)

## Built on by (most cited first, 3 of 3)

- [HistNERo: Historical Named Entity Recognition for the Romanian Language](https://mlsanity.com/paper/2405.00155/) (2024)
- [RoLargeSum: A Large Dialect-Aware Romanian News Dataset for Summary, Headline, and Keyword Generation](https://mlsanity.com/paper/2412.11317/) (2024)
- [Scaling Federated Learning Solutions with Kubernetes for Synthesizing Histopathology Images](https://mlsanity.com/paper/2504.04130/) (2025)

## Abstract (arXiv)

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.