Publications (16)
The Anatomy of Speech Persuasion: Linguistic Shifts in LLM-Modified Speeches
Alisa Barkar, Mathieu Chollet, Matthieu Labeau +2
This study examines how large language models understand the concept of persuasiveness in public speaking by modifying speech transcripts from PhD candidates in the "Ma These en 18…
Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss
Paul Krzakala, Junjie Yang, Rémi Flamary +3
We propose Any2graph, a generic framework for end-to-end Supervised Graph Prediction (SGP) i.e. a deep learning model that predicts an entire graph for any kind of input. The frame…
A survey of neural models for the automatic analysis of conversation: Towards a better integration of the social sciences
Chloé Clavel, Matthieu Labeau, Justine Cassell
Some exciting new approaches to neural architectures for the analysis of conversation have been introduced over the past couple of years. These include neural architectures for det…
Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks
Gaël Guibon, Matthieu Labeau, Hélène Flamein +2
Several recent studies on dyadic human-human interactions have been done on conversations without specific business objectives. However, many companies might benefit from studies d…
Hierarchical Pre-training for Sequence Labelling in Spoken Dialog
Emile Chapuis, Pierre Colombo, Matteo Manica +2
Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key component of spoken dialog systems. In this work, we propose a new approach to learn generic…
Learning Differentiable Surrogate Losses for Structured Prediction
Junjie Yang, Matthieu Labeau, Florence d'Alché-Buc
Structured prediction involves learning to predict complex structures rather than simple scalar values. The main challenge arises from the non-Euclidean nature of the output space,…
Code-switched inspired losses for generic spoken dialog representations
Emile Chapuis, Pierre Colombo, Matthieu Labeau +1
Spoken dialog systems need to be able to handle both multiple languages and multilinguality inside a conversation (\textit{e.g} in case of code-switching). In this work, we introdu…
Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference
Roman Plaud, Alexandre Perez-Lebel, Antoine Saillenfest +4
Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation. This disconnect is especiall…
Revisiting Hierarchical Text Classification: Inference and Metrics
Roman Plaud, Matthieu Labeau, Antoine Saillenfest +1
Hierarchical text classification (HTC) is the task of assigning labels to a text within a structured space organized as a hierarchy. Recent works treat HTC as a conventional multil…
To Each Metric Its Decoding: Post-Hoc Optimal Decision Rules of Probabilistic Hierarchical Classifiers
Roman Plaud, Alexandre Perez-Lebel, Matthieu Labeau +2
Hierarchical classification offers an approach to incorporate the concept of mistake severity by leveraging a structured, labeled hierarchy. However, decoding in such settings freq…
Exploiting Edge Features in Graphs with Fused Network Gromov-Wasserstein Distance
Junjie Yang, Matthieu Labeau, Florence d'Alché-Buc
Pairwise comparison of graphs is key to many applications in Machine learning ranging from clustering, kernel-based classification/regression and more recently supervised graph pre…
The importance of fillers for text representations of speech transcripts
Tanvi Dinkar, Pierre Colombo, Matthieu Labeau +1
While being an essential component of spoken language, fillers (e.g."um" or "uh") often remain overlooked in Spoken Language Understanding (SLU) tasks. We explore the possibility o…
The Impact of Word Splitting on the Semantic Content of Contextualized Word Representations
Aina Garà Soler, Matthieu Labeau, Chloé Clavel
When deriving contextualized word representations from language models, a decision needs to be made on how to obtain one for out-of-vocabulary (OOV) words that are segmented into s…
EmoDynamiX: Emotional Support Dialogue Strategy Prediction by Modelling MiXed Emotions and Discourse Dynamics
Chenwei Wan, Matthieu Labeau, Chloé Clavel
Designing emotionally intelligent conversational systems to provide comfort and advice to people experiencing distress is a compelling area of research. Recently, with advancements…
Improving Multimodal fusion via Mutual Dependency Maximisation
Pierre Colombo, Emile Chapuis, Matthieu Labeau +1
Multimodal sentiment analysis is a trending area of research, and the multimodal fusion is one of its most active topic. Acknowledging humans communicate through a variety of chann…
Compositional Languages Emerge in a Neural Iterated Learning Model
Yi Ren, Shangmin Guo, Matthieu Labeau +2
The principle of compositionality, which enables natural language to represent complex concepts via a structured combination of simpler ones, allows us to convey an open-ended set…