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Recently, various unsupervised representation learning approaches have been investigated to produce augmenting features for natural language processing systems in the open-domain learning scenarios. In this paper, we propose a dynamic …...

AbstractOur goal is to evaluate the usefulness of unsupervised representation learning techniques for detecting stances of Fake News. Therefore we examine several pre-trained language models with respect to their performance …...

Learning low-dimensional representations of networked documents is a crucial task for doc- uments linked in network structures. Rela- tional Topic Models (RTMs) have shown their strengths in modeling both document …...

AbstractUnsupervised representation learning of words from large multilingual corpora is useful for downstream tasks such as word sense disambiguation, semantic text similarity, and information retrieval. The representation precision of log-bilinear …...