7 citations · 13 across the 4 of their papers we have counts for
5 papers
Introducing the Hidden Neural Markov Chain framework
Elie Azeraf, Emmanuel Monfrini, Emmanuel Vignon +1
Nowadays, neural network models achieve state-of-the-art results in many areas as computer vision or speech processing. For sequential data, especially for Natural Language Process…
Hidden Markov Chains, Entropic Forward-Backward, and Part-Of-Speech Tagging
Elie Azeraf, Emmanuel Monfrini, Emmanuel Vignon +1
The ability to take into account the characteristics - also called features - of observations is essential in Natural Language Processing (NLP) problems. Hidden Markov Chain (HMC)…
Heavy-tailed Representations, Text Polarity Classification & Data Augmentation
Hamid Jalalzai, Pierre Colombo, Chloé Clavel +4
The dominant approaches to text representation in natural language rely on learning embeddings on massive corpora which have convenient properties such as compositionality and dist…
Guider l'attention dans les modeles de sequence a sequence pour la prediction des actes de dialogue
Pierre Colombo, Emile Chapuis, Matteo Manica +3
The task of predicting dialog acts (DA) based on conversational dialog is a key component in the development of conversational agents. Accurately predicting DAs requires a precise…
Guiding attention in Sequence-to-sequence models for Dialogue Act prediction
Pierre Colombo, Emile Chapuis, Matteo Manica +3
The task of predicting dialog acts (DA) based on conversational dialog is a key component in the development of conversational agents. Accurately predicting DAs requires a precise…