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Found 8 papers, 2 papers with code
K-AID: Enhancing Pre-trained Language Models with Domain Knowledge for Question Answering
Knowledge enhanced pre-trained language models (K-PLMs) are shown to be effective for many public tasks in the literature but few of them have been successfully applied in practice.
TCIC: Theme Concepts Learning Cross Language and Vision for Image Captioning
Considering that theme concepts can be learned from both images and captions, we propose two settings for their representations learning based on TTN.
Neural Deepfake Detection with Factual Structure of Text
To address this, we propose a graph-based model that utilizes the factual structure of a document for deepfake detection of text.
Look, Listen, and Attend: Co-Attention Network for Self-Supervised Audio-Visual Representation Learning
When watching videos, the occurrence of a visual event is often accompanied by an audio event, e. g., the voice of lip motion, the music of playing instruments.
Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection
We study the detection of propagandistic text fragments in news articles.
Storytelling from an Image Stream Using Scene Graphs
In particular, on the within-image level, we employ a Graph Convolution Network (GCN) to enrich local fine-grained region representations of objects on scene graphs.
Ranked #7 on
Visual Storytelling
on VIST
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa.
Ranked #1 on
Entity Typing
on Open Entity
Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication
Visual storytelling aims to generate a narrative paragraph from a sequence of images automatically.
Ranked #9 on
Visual Storytelling
on VIST
