In this paper, we tackle the temporal knowledge graph completion task by proposing TempCaps, which is a Capsule network-based embedding model for Temporal knowledge graph completion.
We further explore the applicability of our clustering approach by evaluating it on 14 datasets with more diverse topics, text lengths, and numbers of classes.
To this end, we propose ForecastTKGQA, a TKGQA model that employs a TKG forecasting module for future inference, to answer all three types of questions.
A cosmic ray consists of mostly highly energetic protons that emanate from the sun, the Milky Way and distant galaxies.
3D-RETR is capable of 3D reconstruction from a single view or multiple views.
We combine a human evaluation of individual word substitutions and a probabilistic analysis to show that between 96% and 99% of the analyzed attacks do not preserve semantics, indicating that their success is mainly based on feeding poor data to the model.
In this paper, we present an approach to improve the robustness of BERT language models against word substitution-based adversarial attacks by leveraging adversarial perturbations for self-supervised contrastive learning.
We present Knowledge Enhanced Multimodal BART (KM-BART), which is a Transformer-based sequence-to-sequence model capable of reasoning about commonsense knowledge from multimodal inputs of images and texts.
Generating adversarial examples for natural language is hard, as natural language consists of discrete symbols, and examples are often of variable lengths.
Neural network-based dialog systems are attracting increasing attention in both academia and industry.
Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain.