In the realm of human communication, feedback plays a pivotal role in shaping the dynamics of conversations. This study delves into the multifaceted relationship between listener feedback, narration quality and distraction effects. We present an analysis conducted on the SMYLE corpus, specifically enriched for this study, where 30 dyads of participants engaged in 1) face-to-face storytelling (8.2 hours) followed by 2) a free conversation (7.8 hours). The storytelling task unfolds in two conditions, where a storyteller engages with either a “normal” or a “distracted” listener. Examining the feedback impact on storytellers, we discover a positive correlation between the frequency of specific feedback and the narration quality in normal conditions, providing an encouraging conclusion regarding the enhancement of interaction through specific feedback in distraction-free settings. In contrast, in distracted settings, a negative correlation emerges, suggesting that increased specific feedback may disrupt narration quality, underscoring the complexity of feedback dynamics in human communication. The contribution of this paper is twofold: first presenting a new and highly enriched resource for the analysis of discourse phenomena in controlled and normal conditions; second providing new results on feedback production, its form and its consequence on the discourse quality (with direct applications in human-machine interaction).

The aim of this study is to investigate conversational feedbacks that contain smiles and laughs. Firstly, we propose a statistical analysis of smiles and laughs used as generic and specific feedbacks in a corpus of French talk-in-interaction. Our results show that smiles of low intensity are preferentially used to produce generic feedbacks while high intensity smiles and laughs are preferentially used to produce specific feedbacks. Secondly, based on a machine learning approach, we propose a hierarchical classification of feedback to automatically predict not only the presence/absence of a smile but, also the type of smiles according to an intensity-scale (low or high).

The smiling synchrony of the French audio-video conversational corpora “PACO” and “Cheese!” is investigated. The two corpora merged altogether last 6 hours and are made of 25 face-to-face dyadic interactions annotated following the 5 levels Smiling Intensity Scale proposed by Gironzetti et al. (2016). After introducing new indicators for characterizing synchrony phenomena, we find that almost all the 25 interactions of PACO-CHEESE show a strong and significant smiling synchrony behavior. We investigate in a second step the evolution of the synchrony parameters throughout the interaction. No effect is found and it appears rather that the smiling synchrony is present at the very start of the interaction and remains unchanged throughout the conversation.

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.

PAC0 is a French audio-video conversational corpus made of 15 face-to-face dyadic interactions, lasting around 20 min each. This compared corpus has been created in order to explore the impact of the lack of personal common ground (Clark, 1996) on participants collaboration during conversation and specifically on their smile during topic transitions. We have constituted this conversational corpus " PACO” by replicating the experimental protocol of “Cheese!” (Priego-valverde & al.,2018). The only difference that distinguishes these two corpora is the degree of CG of the interlocutors: in Cheese! interlocutors are friends, while in PACO they do not know each other. This experimental protocol allows to analyze how the participants are getting acquainted. This study brings two main contributions. First, the PACO conversational corpus enables to compare the impact of the interlocutors’ common ground. Second, the semi-automatic smile annotation protocol allows to obtain reliable and reproducible smile annotations while reducing the annotation time by a factor 10. Keywords : Common ground, spontaneous interaction, smile, automatic detection.

The question of the type of text used as primary data in treebanks is of certain importance. First, it has an influence at the discourse level: an article is not organized in the same way as a novel or a technical document. Moreover, it also has consequences in terms of semantic interpretation: some types of texts can be easier to interpret than others. We present in this paper a new type of treebank which presents the particularity to answer to specific needs of experimental linguistic. It is made of short texts (book backcovers) that presents a strong coherence in their organization and can be rapidly interpreted. This type of text is adapted to short reading sessions, making it easy to acquire physiological data (e.g. eye movement, electroencepholagraphy). Such a resource offers reliable data when looking for correlations between computational models and human language processing.

The question of how to compare languages and more generally the domain of linguistic typology, relies on the study of different linguistic properties or phenomena. Classically, such a comparison is done semi-manually, for example by extracting information from databases such as the WALS. However, it remains difficult to identify precisely regular parameters, available for different languages, that can be used as a basis towards modeling. We propose in this paper, focusing on the question of syntactic typology, a method for automatically extracting such parameters from treebanks, bringing them into a typology perspective. We present the method and the tools for inferring such information and navigating through the treebanks. The approach has been applied to 10 languages of the Universal Dependencies Treebank. We approach is evaluated by showing how automatic classification correlates with language families.

La typologie des langues repose sur l’étude de la réalisation de propriétés ou phénomènes linguistiques dans plusieurs langues ou familles de langues. Nous abordons dans cet article la question de la typologie syntaxique et proposons une méthode permettant d’extraire automatiquement ces propriétés à partir de treebanks, puis de les analyser en vue de dresser une telle typologie. Nous décrivons cette méthode ainsi que les outils développés pour la mettre en œuvre. Celle-ci a été appliquée à l’analyse de 10 langues décrites dans le Universal Dependencies Treebank. Nous validons ces résultats en montrant comment une technique de classification permet, sur la base des informations extraites, de reconstituer des familles de langues.

Nous présentons ici 4-couv, un nouveau corpus arboré d’environ 3 500 phrases, constitué d’un ensemble de quatrièmes de couverture, étiqueté et analysé automatiquement puis corrigé et validé à la main. Il répond à des besoins spécifiques pour des projets de linguistique expérimentale, et vise à rester compatible avec les autres treebanks existants pour le français. Nous présentons ici le corpus lui-même ainsi que les outils utilisés pour les différentes étapes de son élaboration : choix des textes, étiquetage, parsing, correction manuelle.

Large annotation projects, typically those addressing the question of multimodal annotation in which many different kinds of information have to be encoded, have to elaborate precise and high level annotation schemes. Doing this requires first to define the structure of the information: the different objects and their organization. This stage has to be as much independent as possible from the coding language constraints. This is the reason why we propose a preliminary formal annotation model, represented with typed feature structures. This representation requires a precise definition of the different objects, their properties (or features) and their relations, represented in terms of type hierarchies. This approach has been used to specify the annotation scheme of a large multimodal annotation project (OTIM) and experimented in the annotation of a multimodal corpus (CID, Corpus of Interactional Data). This project aims at collecting, annotating and exploiting a dialogue video corpus in a multimodal perspective (including speech and gesture modalities). The corpus itself, is made of 8 hours of dialogues, fully transcribed and richly annotated (phonetics, syntax, pragmatics, gestures, etc.).

Nous montrons dans cet article qu’il existe une corrélation étroite existant entre la qualité de l’étiquetage morpho-syntaxique et les performances des chunkers. Cette corrélation devient linéaire lorsque la taille des chunks est limitée. Nous appuyons notre démonstration sur la base d’une expérimentation conduite suite à la campagne d’évaluation Passage 2007 (de la Clergerie et al., 2008). Nous analysons pour cela les comportements de deux analyseurs ayant participé à cette campagne. L’interprétation des résultats montre que la tâche de chunking, lorsqu’elle vise des chunks courts, peut être assimilée à une tâche de “super-étiquetage”.

Les méthodes d’analyse syntaxiques hybrides, reposant à la fois sur des techniques statistiques et symboliques, restent peu exploitées. Dans la plupart des cas, les informations statistiques sont intégrées à un squelette contextfree et sont utilisées pour contrôler le choix des règles ou des structures. Nous proposons dans cet article une méthode permettant de calculer un indice de corrélation entre deux objets linguistiques (catégories, propriétés). Nous décrivons une utilisation de cette notion dans le cadre de l’analyse des Grammaires de Propriétés. L’indice de corrélation nous permet dans ce cas de contrôler à la fois la sélection des constituants d’une catégorie, mais également la satisfaction des propriétés qui la décrivent.

Nous présentons une plateforme de développement de lexique offrant une base lexicale accompagnée d’un certain nombre d’outils de maintenance et d’utilisation. Cette base, qui comporte aujourd’hui 440.000 formes du Français contemporain, est destinée à être diffusée et remise à jour régulièrement. Nous exposons d’abord les outils et les techniques employées pour sa constitution et son enrichissement, notamment la technique de calcul des fréquences lexicales par catégorie morphosyntaxique. Nous décrivons ensuite différentes approches pour constituer un sous-lexique de taille réduite, dont la particularité est de couvrir plus de 90% de l’usage. Un tel lexique noyau offre en outre la possibilité d’être réellement complété manuellement avec des informations sémantiques, de valence, pragmatiques etc.