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Found 10 papers, 7 papers with code
Continual Learning for Temporal-Sensitive Question Answering
In this study, we explore an emerging research area of Continual Learning for Temporal Sensitive Question Answering (CLTSQA).
Re-Temp: Relation-Aware Temporal Representation Learning for Temporal Knowledge Graph Completion
Temporal Knowledge Graph Completion (TKGC) under the extrapolation setting aims to predict the missing entity from a fact in the future, posing a challenge that aligns more closely with real-world prediction problems.
Graph Neural Networks for Text Classification: A Survey
Text Classification is the most essential and fundamental problem in Natural Language Processing.
InducT-GCN: Inductive Graph Convolutional Networks for Text Classification
Under the extreme settings with no extra resource and limited amount of training set, can we still learn an inductive graph-based text classification model?
ME-GCN: Multi-dimensional Edge-Embedded Graph Convolutional Networks for Semi-supervised Text Classification
Our ME-GCN can integrate a rich source of graph edge information of the entire text corpus.
Understanding Graph Convolutional Networks for Text Classification
Graph Convolutional Networks (GCN) have been effective at tasks that have rich relational structure and can preserve global structure information of a dataset in graph embeddings.
CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection
Accompanying the dataset is a thorough in-game toxicity analysis, which provides comprehensive understanding of context at utterance, token, and dual levels.
VICTR: Visual Information Captured Text Representation for Text-to-Vision Multimodal Tasks
We propose a new visual contextual text representation for text-to-image multimodal tasks, VICTR, which captures rich visual semantic information of objects from the text input.
Detect All Abuse! Toward Universal Abusive Language Detection Models
Online abusive language detection (ALD) has become a societal issue of increasing importance in recent years.
VICTR: Visual Information Captured Text Representation for Text-to-Image Multimodal Tasks
We propose a new visual contextual text representation for text-to-image multimodal tasks, VICTR, which captures rich visual semantic information of objects from the text input.
Ranked #23 on
Text-to-Image Generation
on MS COCO
(Inception score metric)
