Magalie Ochs


2020

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Multimodal Corpus of Bidirectional Conversation of Human-human and Human-robot Interaction during fMRI Scanning
Birgit Rauchbauer | Youssef Hmamouche | Brigitte Bigi | Laurent Prévot | Magalie Ochs | Thierry Chaminade
Proceedings of the 12th Language Resources and Evaluation Conference

In this paper we present investigation of real-life, bi-directional conversations. We introduce the multimodal corpus derived from these natural conversations alternating between human-human and human-robot interactions. The human-robot interactions were used as a control condition for the social nature of the human-human conversations. The experimental set up consisted of conversations between the participant in a functional magnetic resonance imaging (fMRI) scanner and a human confederate or conversational robot outside the scanner room, connected via bidirectional audio and unidirectional videoconferencing (from the outside to inside the scanner). A cover story provided a framework for natural, real-life conversations about images of an advertisement campaign. During the conversations we collected a multimodal corpus for a comprehensive characterization of bi-directional conversations. In this paper we introduce this multimodal corpus which includes neural data from functional magnetic resonance imaging (fMRI), physiological data (blood flow pulse and respiration), transcribed conversational data, as well as face and eye-tracking recordings. Thus, we present a unique corpus to study human conversations including neural, physiological and behavioral data.

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The Brain-IHM Dataset: a New Resource for Studying the Brain Basis of Human-Human and Human-Machine Conversations
Magalie Ochs | Roxane Bertrand | Aurélie Goujon | Deirdre Bolger | Anne-Sophie Dubarry | Philippe Blache
Proceedings of the 12th Language Resources and Evaluation Conference

This paper presents an original dataset of controlled interactions, focusing on the study of feedback items. It consists on recordings of different conversations between a doctor and a patient, played by actors. In this corpus, the patient is mainly a listener and produces different feedbacks, some of them being (voluntary) incongruent. Moreover, these conversations have been re-synthesized in a virtual reality context, in which the patient is played by an artificial agent. The final corpus is made of different movies of human-human conversations plus the same conversations replayed in a human-machine context, resulting in the first human-human/human-machine parallel corpus. The corpus is then enriched with different multimodal annotations at the verbal and non-verbal levels. Moreover, and this is the first dataset of this type, we have designed an experiment during which different participants had to watch the movies and give an evaluation of the interaction. During this task, we recorded participant’s brain signal. The Brain-IHM dataset is then conceived with a triple purpose: 1/ studying feedbacks by comparing congruent vs. incongruent feedbacks 2/ comparing human-human and human-machine production of feedbacks 3/ studying the brain basis of feedback perception.

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BrainPredict: a Tool for Predicting and Visualising Local Brain Activity
Youssef Hmamouche | Laurent Prévot | Magalie Ochs | Thierry Chaminade
Proceedings of the 12th Language Resources and Evaluation Conference

In this paper, we present a tool allowing dynamic prediction and visualization of an individual’s local brain activity during a conversation. The prediction module of this tool is based on classifiers trained using a corpus of human-human and human-robot conversations including fMRI recordings. More precisely, the module takes as input behavioral features computed from raw data, mainly the participant and the interlocutor speech but also the participant’s visual input and eye movements. The visualisation module shows in real-time the dynamics of brain active areas synchronised with the behavioral raw data. In addition, it shows which integrated behavioral features are used to predict the activity in individual brain areas.

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Two-level classification for dialogue act recognition in task-oriented dialogues
Philippe Blache | Massina Abderrahmane | Stéphane Rauzy | Magalie Ochs | Houda Oufaida
Proceedings of the 28th International Conference on Computational Linguistics

Dialogue act classification becomes a complex task when dealing with fine-grain labels. Many applications require such level of labelling, typically automatic dialogue systems. We present in this paper a 2-level classification technique, distinguishing between generic and specific dialogue acts (DA). This approach makes it possible to benefit from the very good accuracy of generic DA classification at the first level and proposes an efficient approach for specific DA, based on high-level linguistic features. Our results show the interest of involving such features into the classifiers, outperforming all other feature sets, in particular those classically used in DA classification.

2018

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LIS at SemEval-2018 Task 2: Mixing Word Embeddings and Bag of Features for Multilingual Emoji Prediction
Gaël Guibon | Magalie Ochs | Patrice Bellot
Proceedings of The 12th International Workshop on Semantic Evaluation

In this paper we present the system submitted to the SemEval2018 task2 : Multilingual Emoji Prediction. Our system approaches both languages as being equal by first; considering word embeddings associated to automatically computed features of different types, then by applying bagging algorithm RandomForest to predict the emoji of a tweet.

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A Semi-autonomous System for Creating a Human-Machine Interaction Corpus in Virtual Reality: Application to the ACORFORMed System for Training Doctors to Break Bad News
Magalie Ochs | Philippe Blache | Grégoire de Montcheuil | Jean-Marie Pergandi | Jorane Saubesty | Daniel Francon | Daniel Mestre
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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De l’usage réel des emojis à une prédiction de leurs catégories (From Emoji Usage to Emoji-Category Prediction)
Gaël Guibon | Magalie Ochs | Patrice Bellot
Actes de la Conférence TALN. Volume 1 - Articles longs, articles courts de TALN

L’utilisation des emojis dans les messageries sociales n’a eu de cesse d’augmenter ces dernières années. Plusieurs travaux récents ont porté sur la prédiction d’emojis afin d’épargner à l’utillisateur le parcours de librairies d’emojis de plus en plus conséquentes. Nous proposons une méthode permettant de récupérer automatiquement les catégories d’emojis à partir de leur contexte d’utilisation afin d’améliorer la prédiction finale. Pour ce faire nous utilisons des plongements lexicaux en considérant les emojis comme des mots présents dans des tweets. Nous appliquons ensuite un regroupement automatique restreint aux emojis visages afin de vérifier l’adéquation des résultats avec la théorie d’Ekman. L’approche est reproductible et applicable sur tous types d’emojis, ou lorsqu’il est nécessaire de prédire de nombreuses classes.

2017

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Une plateforme de recommandation automatique d’emojis (An emoji recommandation platform)
Gaël Guibon | Magalie Ochs | Patrice Bellot
Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 3 - Démonstrations

Nous présentons une interface de recommandation d’emojis porteurs de sentiments qui utilise un modèle de prédiction appris sur des messages informels privés. Chacun étant associé à deux scores de polarité prédits. Cette interface permet permet également d’enregistrer les choix de l’utilisateur pour confirmer ou infirmer la recommandation.

2014

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Mining a multimodal corpus for non-verbal behavior sequences conveying attitudes
Mathieu Chollet | Magalie Ochs | Catherine Pelachaud
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Interpersonal attitudes are expressed by non-verbal behaviors on a variety of different modalities. The perception of these behaviors is influenced by how they are sequenced with other behaviors from the same person and behaviors from other interactants. In this paper, we present a method for extracting and generating sequences of non-verbal signals expressing interpersonal attitudes. These sequences are used as part of a framework for non-verbal expression with Embodied Conversational Agents that considers different features of non-verbal behavior: global behavior tendencies, interpersonal reactions, sequencing of non-verbal signals, and communicative intentions. Our method uses a sequence mining technique on an annotated multimodal corpus to extract sequences characteristic of different attitudes. New sequences of non-verbal signals are generated using a probabilistic model, and evaluated using the previously mined sequences.

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A model to generate adaptive multimodal job interviews with a virtual recruiter
Zoraida Callejas | Brian Ravenet | Magalie Ochs | Catherine Pelachaud
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper presents an adaptive model of multimodal social behavior for embodied conversational agents. The context of this research is the training of youngsters for job interviews in a serious game where the agent plays the role of a virtual recruiter. With the proposed model the agent is able to adapt its social behavior according to the anxiety level of the trainee and a predefined difficulty level of the game. This information is used to select the objective of the system (to challenge or comfort the user), which is achieved by selecting the complexity of the next question posed and the agent’s verbal and non-verbal behavior. We have carried out a perceptive study that shows that the multimodal behavior of an agent implementing our model successfully conveys the expected social attitudes.