Reinforcement adaptation of an attention-based neural natural language generator for spoken dialogue systems

Following some recent propositions to handle natural language generation in spoken dialogue systems with long short-term memory recurrent neural network models~\citep{Wen2016a} we first investigate a variant thereof with the objective of a better integration of the attention subnetwork. Then our next objective is to propose and evaluate a framework to adapt the NLG module online through direct interactions with the users. When doing so the basic way is to ask the user to utter an alternative sentence to express a particular dialogue act. But then the system has to decide between using an automatic transcription or to ask for a manual transcription. To do so a reinforcement learning approach based on an adversarial bandit scheme is retained. We show that by defining appropriately the rewards as a linear combination of expected payoffs and costs of acquiring the new data provided by the user, a system design can balance between improving the system's performance towards a better match with the user's preferences and the burden associated with it. Then the actual benefits of this system is assessed with a human evaluation, showing that the addition of more diverse utterances allows to produce sentences more satisfying for the user

Semaine d'Etude Mathématiques et Entreprises 6 : Analyse statistique des défauts en électronique analogique

Nous nous intéressons à des données issues de mesures de tensions sur des circuits électroniques analogiques. Plus précisément, il s'agit de proposer une analyse de courbes représentant l'évolution en fonction du temps des tensions en différents nœuds d'un circuit électronique. Notre objectif est de proposer une analyse automatisée de la qualité des courbes. Plus précisément, nous proposons ici des méthodes statistiques d'analyse de données capable de : -- Identifier d'éventuels patterns dans les courbes (classification), -- Isoler les courbes présentant des "anomalies" (détection de courbes suspectes)

Analysis of third-order nonlinearity effects in very high-Q WGM resonator cavity ringdown spectroscopy

International audienceThe time domain coupled-mode theory (CMT) is applied to the analysis of the dynamic of third-order optical nonlinear effects in high-finesse whispering-gallery-mode (WGM) resonators. We show that this model is well adapted to the analysis of cavity-ringdown spectroscopy signal under modal-coupling due to Rayleigh backscattering in linear and nonlinear regimes. The experiments are carried out in silica WGM microspheres. Considering thermal and Kerr effects, CMT simulations are in good agreement, with experimental results for input power up to about 1 mW. For well-known optical materials such as silica, this experimental data analysis method can be used to measure the quality factor, the coupling regime, and the mode volume of high-finesse WGM. Furthermore, this technique could be developed to infer linear and nonlinear properties of high-finesse coated WGM microspheres

Predicting the Semantic Textual Similarity with Siamese CNN and LSTM

National audienceSemantic Textual Similarity (STS) is the basis of many applications in Natural Language Processing (NLP). Our system combines convolution and recurrent neural networks to measure the semantic similarity of sentences. It uses a convolution network to take account of the local context of words and an LSTM to consider the global context of sentences. This combination of networks helps to preserve the relevant information of sentences and improves the calculation of the similarity between sentences. Our model has achieved good results and is competitive with the best state-of-the-art systems.La Similarité Textuelle Sémantique (STS) est la base de nombreuses applications dans le Traitement Automatique du Langage Naturel (TALN). Notre système combine des réseaux neuronaux convolutifs et récurrents pour mesurer la similarité sémantique des phrases. Il utilise un réseau convolutif pour tenir compte du contexte local des mots et un LSTM pour prendre en considération le contexte global d'une phrase. Cette combinaison des réseaux préserve mieux les informations significatives des phrases et améliore le calcul de la similarité entre les phrases. Notre modèle a obtenu de bons résultats et est compétitif avec les meilleurs systèmes de l'état de l'art

Automatic Text Summarization with a Reduced Vocabulary Using Continuous Space Vectors

poster paperInternational audienceIn this paper, we propose a new method that uses continuous vectors to map words to a reduced vocabulary, in the context of Automatic Text Summarization (ATS). This method is evaluated on the MultiLing corpus by the ROUGE evaluation measures with four ATS systems. Our experiments show that the reduced vocabulary improves the performance of state-of-the-art systems

Microblog Contextualization Using Continuous Space Vectors: Multi-Sentence Compression of Cultural Documents

International audienceIn this paper we describe our work for the MC2 CLEF 2017 lab. We participated in the content analysis task that involves filtering, language recognition and summarization. We combine Information Retrieval with Multi-Sentence Compression methods to contextualize mi-croblogs using Wikipedia's pages