72 citations · 161 across the 4 of their papers we have counts for
7 papers
Learning representations with end-to-end models for improved remaining useful life prognostics
Alaaeddine Chaoub, Alexandre Voisin, Christophe Cerisara +1
The remaining Useful Life (RUL) of equipment is defined as the duration between the current time and its failure. An accurate and reliable prognostic of the remaining useful life p…
On the Effects of Using word2vec Representations in Neural Networks for Dialogue Act Recognition
Christophe Cerisara, Pavel Kral, Ladislav Lenc
Dialogue act recognition is an important component of a large number of natural language processing pipelines. Many research works have been carried out in this area, but relativel…
Cross-lingual Approaches for Task-specific Dialogue Act Recognition
Jiří Martínek, Christophe Cerisara, Pavel Král +1
In this paper we exploit cross-lingual models to enable dialogue act recognition for specific tasks with a small number of annotations. We design a transfer learning approach for d…
Quality of syntactic implication of RL-based sentence summarization
Hoa T. Le, Christophe Cerisara, Claire Gardent
Work on summarization has explored both reinforcement learning (RL) optimization using ROUGE as a reward and syntax-aware models, such as models those input is enriched with part-o…
Multi-lingual Dialogue Act Recognition with Deep Learning Methods
Jiří Martínek, Pavel Král, Ladislav Lenc +1
This paper deals with multi-lingual dialogue act (DA) recognition. The proposed approaches are based on deep neural networks and use word2vec embeddings for word representation. Tw…
Multi-task dialog act and sentiment recognition on Mastodon
Christophe Cerisara, Somayeh Jafaritazehjani, Adedayo Oluokun +1
Because of license restrictions, it often becomes impossible to strictly reproduce most research results on Twitter data already a few months after the creation of the corpus. This…
Do Convolutional Networks need to be Deep for Text Classification ?
Hoa T. Le, Christophe Cerisara, Alexandre Denis
We study in this work the importance of depth in convolutional models for text classification, either when character or word inputs are considered. We show on 5 standard text class…