FreSaDa: A French Satire Data Set for Cross-Domain Satire Detection
| Autor: | Adrian-Gabriel Chifu, Radu Tudor Ionescu |
|---|---|
| Přispěvatelé: | Universitatea din Bucuresti (UB), Recherche d’information et Interactions (R2I), Laboratoire d'Informatique et Systèmes (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU) |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Science - Computation and Language text classification Artificial neural network Computer science business.industry Dot product computer.software_genre satire detection Domain (software engineering) [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Machine Learning (cs.LG) Data set Character (mathematics) Task analysis Pairwise comparison unsupervised domain adaptation Artificial intelligence cross-domain evaluation business computer Computation and Language (cs.CL) Natural language processing Word (computer architecture) |
| Zdroj: | The International Joint Conference on Neural Network, IJCNN 2021 The International Joint Conference on Neural Network, IJCNN 2021, Jul 2021, Virtual Event, United States IJCNN |
| DOI: | 10.48550/arxiv.2104.04828 |
| Popis: | In this paper, we introduce FreSaDa, a French Satire Data Set, which is composed of 11,570 articles from the news domain. In order to avoid reporting unreasonably high accuracy rates due to the learning of characteristics specific to publication sources, we divided our samples into training, validation and test, such that the training publication sources are distinct from the validation and test publication sources. This gives rise to a cross-domain (cross-source) satire detection task. We employ two classification methods as baselines for our new data set, one based on low-level features (character n-grams) and one based on high-level features (average of CamemBERT word embeddings). As an additional contribution, we present an unsupervised domain adaptation method based on regarding the pairwise similarities (given by the dot product) between the training samples and the validation samples as features. By including these domain-specific features, we attain significant improvements for both character n-grams and CamemBERT embeddings. Comment: Accepted at IJCNN 2021 |
| Databáze: | OpenAIRE |
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