114 citations · 390 across the 23 of their papers we have counts for
5 papers · 1 filter
Automatic Generation of Grounded Visual Questions
Shijie Zhang, Lizhen Qu, Shaodi You +2
In this paper, we propose the first model to be able to generate visually grounded questions with diverse types for a single image. Visual question generation is an emerging topic…
Named Entity Recognition for Novel Types by Transfer Learning
Lizhen Qu, Gabriela Ferraro, Liyuan Zhou +2
In named entity recognition, we often don't have a large in-domain training corpus or a knowledge base with adequate coverage to train a model directly. In this paper, we propose a…
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
Giorgio Patrini, Alessandro Rozza, Aditya Menon +2
We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise. We propose two procedures for loss…
STransE: a novel embedding model of entities and relationships in knowledge bases
Dat Quoc Nguyen, Kairit Sirts, Lizhen Qu +1
Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases…
Neighborhood Mixture Model for Knowledge Base Completion
Dat Quoc Nguyen, Kairit Sirts, Lizhen Qu +1
Knowledge bases are useful resources for many natural language processing tasks, however, they are far from complete. In this paper, we define a novel entity representation as a mi…