200 citations · 515 across the 16 of their papers we have counts for
5 papers · 1 filter
Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules?
Fréderic Godin, Kris Demuynck, Joni Dambre +2
Character-level features are currently used in different neural network-based natural language processing algorithms. However, little is known about the character-level patterns th…
Predefined Sparseness in Recurrent Sequence Models
Thomas Demeester, Johannes Deleu, Fréderic Godin +1
Inducing sparseness while training neural networks has been shown to yield models with a lower memory footprint but similar effectiveness to dense models. However, sparseness is ty…
Adversarial training for multi-context joint entity and relation extraction
Giannis Bekoulis, Johannes Deleu, Thomas Demeester +1
Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data. We sho…
Prior Attention for Style-aware Sequence-to-Sequence Models
Lucas Sterckx, Johannes Deleu, Chris Develder +1
We extend sequence-to-sequence models with the possibility to control the characteristics or style of the generated output, via attention that is generated a priori (before decodin…
DeepProbLog: Neural Probabilistic Logic Programming
Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig +2
We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning tech…