Papers with deep network

2 papers
Multi-task dialog act and sentiment recognition on Mastodon (C18-1)

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Challenge: Social media are a gold mine for researchers in many domains and especially in natural language processing . license restrictions make it difficult to strictly reproduce research results on Twitter data .
Approach: They propose to annotate a Twitter-like corpus from a decentralized social network with permissive licenses that are compatible with reproducible experiments.
Outcome: The proposed method shows that transfer learning can be efficiently achieved between tasks.
Fake News Detection using Deep Markov Random Fields (N19-1)

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Challenge: Existing deep-learning-based methods ignore the correlations among news articles and only consider each article individually.
Approach: They propose a graph-theoretic method that inherits the power of deep learning while utilizing the correlations among the articles.
Outcome: The proposed model improves on state-of-the-art models on well-known datasets.

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