Search Results for author: Christophe Cerisara

Found 11 papers, 1 papers with code

Learning representations with end-to-end models for improved remaining useful life prognostics

no code implementations11 Apr 2021 Alaaeddine Chaoub, Alexandre Voisin, Christophe Cerisara, Benoît Iung

In this work, we propose an end-to-end deep learning model based on multi-layer perceptron and long short-term memory layers (LSTM) to predict the RUL.

On the Effects of Using word2vec Representations in Neural Networks for Dialogue Act Recognition

no code implementations22 Oct 2020 Christophe Cerisara, Pavel Kral, Ladislav Lenc

The performance of the proposed approach is consistent across these languages and it is comparable to the state-of-the-art results in English.

Word Embeddings

Cross-lingual Approaches for Task-specific Dialogue Act Recognition

no code implementations19 May 2020 Jiří Martínek, Christophe Cerisara, Pavel Král, Ladislav Lenc

In this paper we exploit cross-lingual models to enable dialogue act recognition for specific tasks with a small number of annotations.

Cross-Lingual Transfer Transfer Learning

Quality of syntactic implication of RL-based sentence summarization

no code implementations11 Dec 2019 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-of-speech (POS)-tags and dependency information.

POS Sentence Summarization

Do Convolutional Networks need to be Deep for Text Classification ?

no code implementations13 Jul 2017 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.

Classification General Classification +2

Weakly-supervised text-to-speech alignment confidence measure

no code implementations COLING 2016 Guillaume Serri{\`e}re, Christophe Cerisara, Dominique Fohr, Odile Mella

This work proposes a new confidence measure for evaluating text-to-speech alignment systems outputs, which is a key component for many applications, such as semi-automatic corpus anonymization, lips syncing, film dubbing, corpus preparation for speech synthesis and speech recognition acoustic models training.

Speech Recognition Speech Synthesis

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