13 citations · 32 across the 11 of their papers we have counts for
4 papers · 1 filter
Preventing RNN from Using Sequence Length as a Feature
Jean-Thomas Baillargeon, Hélène Cossette, Luc Lamontagne
Recurrent neural networks are deep learning topologies that can be trained to classify long documents. However, in our recent work, we found a critical problem with these cells: th…
Assessing the Impact of Sequence Length Learning on Classification Tasks for Transformer Encoder Models
Jean-Thomas Baillargeon, Luc Lamontagne
Classification algorithms using Transformer architectures can be affected by the sequence length learning problem whenever observations from different classes have a different leng…
Attending Form and Context to Generate Specialized Out-of-VocabularyWords Representations
Nicolas Garneau, Jean-Samuel Leboeuf, Yuval Pinter +1
We propose a new contextual-compositional neural network layer that handles out-of-vocabulary (OOV) words in natural language processing (NLP) tagging tasks. This layer consists of…
A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well
Nicolas Garneau, Mathieu Godbout, David Beauchemin +2
In this paper, we reproduce the experiments of Artetxe et al. (2018b) regarding the robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. We…