Chloé Clavel


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Multimodal Analysis of Cohesion in Multi-party Interactions
Reshmashree Bangalore Kantharaju | Caroline Langlet | Mukesh Barange | Chloé Clavel | Catherine Pelachaud
Proceedings of the 12th Language Resources and Evaluation Conference

Group cohesion is an emergent phenomenon that describes the tendency of the group members’ shared commitment to group tasks and the interpersonal attraction among them. This paper presents a multimodal analysis of group cohesion using a corpus of multi-party interactions. We utilize 16 two-minute segments annotated with cohesion from the AMI corpus. We define three layers of modalities: non-verbal social cues, dialogue acts and interruptions. The initial analysis is performed at the individual level and later, we combine the different modalities to observe their impact on perceived level of cohesion. Results indicate that occurrence of laughter and interruption are higher in high cohesive segments. We also observe that, dialogue acts and head nods did not have an impact on the level of cohesion by itself. However, when combined there was an impact on the perceived level of cohesion. Overall, the analysis shows that multimodal cues are crucial for accurate analysis of group cohesion.

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The POTUS Corpus, a Database of Weekly Addresses for the Study of Stance in Politics and Virtual Agents
Thomas Janssoone | Kévin Bailly | Gaël Richard | Chloé Clavel
Proceedings of the 12th Language Resources and Evaluation Conference

One of the main challenges in the field of Embodied Conversational Agent (ECA) is to generate socially believable agents. The common strategy for agent behaviour synthesis is to rely on dedicated corpus analysis. Such a corpus is composed of multimedia files of socio-emotional behaviors which have been annotated by external observers. The underlying idea is to identify interaction information for the agent’s socio-emotional behavior by checking whether the intended socio-emotional behavior is actually perceived by humans. Then, the annotations can be used as learning classes for machine learning algorithms applied to the social signals. This paper introduces the POTUS Corpus composed of high-quality audio-video files of political addresses to the American people. Two protagonists are present in this database. First, it includes speeches of former president Barack Obama to the American people. Secondly, it provides videos of these same speeches given by a virtual agent named Rodrigue. The ECA reproduces the original address as closely as possible using social signals automatically extracted from the original one. Both are annotated for social attitudes, providing information about the stance observed in each file. It also provides the social signals automatically extracted from Obama’s addresses used to generate Rodrigue’s ones.

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Hierarchical Pre-training for Sequence Labelling in Spoken Dialog
Emile Chapuis | Pierre Colombo | Matteo Manica | Matthieu Labeau | Chloé Clavel
Findings of the Association for Computational Linguistics: EMNLP 2020

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 representations adapted to spoken dialog, which we evaluate on a new benchmark we call Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE benchmark (SILICONE). SILICONE is model-agnostic and contains 10 different datasets of various sizes. We obtain our representations with a hierarchical encoder based on transformer architectures, for which we extend two well-known pre-training objectives. Pre-training is performed on OpenSubtitles: a large corpus of spoken dialog containing over 2.3 billion of tokens. We demonstrate how hierarchical encoders achieve competitive results with consistently fewer parameters compared to state-of-the-art models and we show their importance for both pre-training and fine-tuning.

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The importance of fillers for text representations of speech transcripts
Tanvi Dinkar | Pierre Colombo | Matthieu Labeau | Chloé Clavel
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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 of representing them with deep contextualised embeddings, showing improvements on modelling spoken language and two downstream tasks — predicting a speaker’s stance and expressed confidence.


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From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining
Alexandre Garcia | Pierre Colombo | Florence d’Alché-Buc | Slim Essid | Chloé Clavel
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

The task of predicting fine grained user opinion based on spontaneous spoken language is a key problem arising in the development of Computational Agents as well as in the development of social network based opinion miners. Unfortunately, gathering reliable data on which a model can be trained is notoriously difficult and existing works rely only on coarsely labeled opinions. In this work we aim at bridging the gap separating fine grained opinion models already developed for written language and coarse grained models developed for spontaneous multimodal opinion mining. We take advantage of the implicit hierarchical structure of opinions to build a joint fine and coarse grained opinion model that exploits different views of the opinion expression. The resulting model shares some properties with attention-based models and is shown to provide competitive results on a recently released multimodal fine grained annotated corpus.


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Automatic Measures to Characterise Verbal Alignment in Human-Agent Interaction
Guillaume Dubuisson Duplessis | Chloé Clavel | Frédéric Landragin
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

This work aims at characterising verbal alignment processes for improving virtual agent communicative capabilities. We propose computationally inexpensive measures of verbal alignment based on expression repetition in dyadic textual dialogues. Using these measures, we present a contrastive study between Human-Human and Human-Agent dialogues on a negotiation task. We exhibit quantitative differences in the strength and orientation of verbal alignment showing the ability of our approach to characterise important aspects of verbal alignment.


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Improving social relationships in face-to-face human-agent interactions: when the agent wants to know user’s likes and dislikes
Caroline Langlet | Chloé Clavel
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)


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Modelling agent’s questions for analysing user’s affects, appreciations and judgements in human-agent interaction (Modélisation des questions de l’agent pour l’analyse des affects, jugements et appréciations de l’utilisateur dans les interactions humain-agent) [in French]
Caroline Langlet | Chloé Clavel
Proceedings of TALN 2014 (Volume 2: Short Papers)

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Comparative analysis of verbal alignment in human-human and human-agent interactions
Sabrina Campano | Jessica Durand | Chloé Clavel
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Engagement is an important feature in human-human and human-agent interaction. In this paper, we investigate lexical alignment as a cue of engagement, relying on two different corpora : CID and SEMAINE. Our final goal is to build a virtual conversational character that could use alignment strategies to maintain user’s engagement. To do so, we investigate two alignment processes : shared vocabulary and other-repetitions. A quantitative and qualitative approach is proposed to characterize these aspects in human-human (CID) and human-operator (SEMAINE) interactions. Our results show that these processes are observable in both corpora, indicating a stable pattern that can be further modelled in conversational agents.


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Quel est l’apport de la détection d’entités nommées pour l’extraction d’information en domaine restreint ? (What is the contribution of named entities detection for information extraction in restricted domain ?) [in French]
Camille Dutrey | Chloé Clavel | Sophie Rosset | Ioana Vasilescu | Martine Adda-Decker
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 2: TALN


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Fear-type emotions of the SAFE Corpus: annotation issues
Chloé Clavel | Ioana Vasilescu | Laurence Devillers | Thibaut Ehrette | Gaël Richard
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

The present research focuses on annotation issues in the context of the acoustic detection of fear-type emotions for surveillance applications. The emotional speech material used for this study comes from the previously collected SAFE Database (Situation Analysis in a Fictional and Emotional Database) which consists of audio-visual sequences extracted from movie fictions. A generic annotation scheme was developed to annotate the various emotional manifestations contained in the corpus. The annotation was carried out by two labellers and the two annotations strategies are confronted. It emerges that the borderline between emotion and neutral vary according to the labeller. An acoustic validation by a third labeller allows at analysing the two strategies. Two human strategies are then observed: a first one, context-oriented which mixes audio and contextual (video) information in emotion categorization; and a second one, based mainly on audio information. The k-means clustering confirms the role of audio cues in human annotation strategies. It particularly helps in evaluating those strategies from the point of view of a detection system based on audio cues.