Papers by Magalie Ochs
Two-level classification for dialogue act recognition in task-oriented dialogues (2020.coling-main)
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| Challenge: | Existing methods for dialogue act classification are limited and feature sets are low . recognizing dialogue acts is useful for identifying type of information and knowledge to be conveyed . |
| Approach: | They propose a 2-level classification technique, distinguishing between generic and specific dialogue acts (DA) they propose an efficient approach for specific DA, based on high-level linguistic features. |
| Outcome: | The proposed method outperforms classical methods for DA classification by including high-level features. |
Multimodal Corpus of Bidirectional Conversation of Human-human and Human-robot Interaction during fMRI Scanning (2020.lrec-1)
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Birgit Rauchbauer, Youssef Hmamouche, Brigitte Bigi, Laurent Prévot, Magalie Ochs, Thierry Chaminade
| Challenge: | a study of real-life bi-directional conversations combines multimodal corpus with neural, physiological and behavioral data. |
| Approach: | They propose a multimodal corpus derived from natural conversations . they used human-human interactions as a control condition . |
| Outcome: | The proposed corpus includes neural, physiological and behavioral data. |
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 (L18-1)
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Magalie Ochs, Philippe Blache, Grégoire de Montcheuil, Jean-Marie Pergandi, Jorane Saubesty, Daniel Francon, Daniel Mestre
| Challenge: | Existing methods for training doctors to break bad news are expensive and time consuming. |
| Approach: | They propose a method to collect a corpus of human-machine interactions and then construct a semi-autonomous system based on the collected corpus. |
| Outcome: | The proposed system is based on a corpus-based method to analyze human-machine interactions and then develop fully autonomous prototype. |
The Brain-IHM Dataset: a New Resource for Studying the Brain Basis of Human-Human and Human-Machine Conversations (2020.lrec-1)
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| Challenge: | Using a dataset of controlled interactions, we have studied the feedback items produced by the interlocutors during a conversation. |
| Approach: | They propose to use a dataset of controlled interactions to study feedback items and a virtual reality context to re-synthesize the conversations. |
| Outcome: | The proposed dataset compares human-human and human-machine production of feedbacks and is the first of its kind. |
Annotation of Communicative Functions of Short Feedback Tokens in Switchboard (2022.lrec-1)
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| Challenge: | lexical forms and prosodic characteristics of short feedback tokens are not indicative of their communicative function. |
| Approach: | They propose to annotate short feedback tokens with a lexical annotation scheme . they find that feedback functions have distinguishable prosodic characteristics . |
| Outcome: | The proposed annotations show that lexical forms alone are not indicative of the communicative function. |
The Distracted Ear: How Listeners Shape Conversational Dynamics (2024.lrec-main)
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| Challenge: | a new study examines the relationship between listener feedback and narration quality in human communication . a variety of complex linguistic and cognitive processes underpin the success of conversations . |
| Approach: | They analyze listener feedback, narration quality and distraction effects in a SMYLE corpus . they find a positive correlation between frequency of specific feedback and narration quality . |
| Outcome: | The proposed method shows that feedback plays a pivotal role in shaping the dynamics of conversations. |
BrainPredict: a Tool for Predicting and Visualising Local Brain Activity (2020.lrec-1)
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| Challenge: | Using fMRI, we recorded a corpus of human-human and human-robot conversations while participants brain activity was recorded with f.MRI, but we did not find any tools for displaying together brain activity prediction of non-controlled conversations, the raw material used in this prediction and the features used for these predictions. |
| Approach: | They propose a tool that allows dynamic prediction and visualization of an individual’s local brain activity during a conversation using raw behavioral data. |
| Outcome: | The proposed tool 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. |