Adversarial Capsule Networks for Romanian Satire Detection and Sentiment
  Analysis
Sebastian-Vasile Echim Răzvan-Alexandru Smădu Andrei-Marius Avram Dumitru-Clementin Cercel Florin Pop
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.