410 citations · 964 across the 27 of their papers we have counts for
5 papers · 1 filter
Avoiding Your Teacher's Mistakes: Training Neural Networks with Controlled Weak Supervision
Mostafa Dehghani, Aliaksei Severyn, Sascha Rothe +1
Training deep neural networks requires massive amounts of training data, but for many tasks only limited labeled data is available. This makes weak supervision attractive, using we…
Learning to Learn from Weak Supervision by Full Supervision
Mostafa Dehghani, Aliaksei Severyn, Sascha Rothe +1
In this paper, we propose a method for training neural networks when we have a large set of data with weak labels and a small amount of data with true labels. In our proposed model…
Neural Ranking Models with Weak Supervision
Mostafa Dehghani, Hamed Zamani, Aliaksei Severyn +2
Despite the impressive improvements achieved by unsupervised deep neural networks in computer vision and NLP tasks, such improvements have not yet been observed in ranking for info…
Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification
Jan Deriu, Aurelien Lucchi, Valeria De Luca +5
This paper presents a novel approach for multi-lingual sentiment classification in short texts. This is a challenging task as the amount of training data in languages other than En…
A Hybrid Convolutional Variational Autoencoder for Text Generation
Stanislau Semeniuta, Aliaksei Severyn, Erhardt Barth
In this paper we explore the effect of architectural choices on learning a Variational Autoencoder (VAE) for text generation. In contrast to the previously introduced VAE model for…