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Found 7 papers, 2 papers with code
Context-aware Style Learning and Content Recovery Networks for Neural Style Transfer
Neural text transfer aims to change the style of a text sequence while keeping its original content.
Zero-shot Cross-lingual Conversational Semantic Role Labeling
While conversational semantic role labeling (CSRL) has shown its usefulness on Chinese conversational tasks, it is still under-explored in non-Chinese languages due to the lack of multilingual CSRL annotations for the parser training.
Unified Dual-view Cognitive Model for Interpretable Claim Verification
From the view of the collective cognition, we not only capture the word-level semantics based on individual users, but also focus on sentence-level semantics (i. e., the overall responses) among all users and adjust the proportion between them to generate global evidence.
DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification
Simultaneously, the discovered evidence only roughly aims at the interpretability of the whole sequence of claims but insufficient to focus on the false parts of claims.
Adaptive Interaction Fusion Networks for Fake News Detection
In this paper, we propose Adaptive Interaction Fusion Networks (AIFN) to fulfill cross-interaction fusion among features for fake news detection.
Discovering Differential Features: Adversarial Learning for Information Credibility Evaluation
A series of deep learning approaches extract a large number of credibility features to detect fake news on the Internet.
Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection
Recently, neural networks based on multi-task learning have achieved promising performance on fake news detection, which focus on learning shared features among tasks as complementary features to serve different tasks.
