170 research outputs found
UPB @ ACTI: Detecting Conspiracies using fine tuned Sentence Transformers
Conspiracy theories have become a prominent and concerning aspect of online
discourse, posing challenges to information integrity and societal trust. As
such, we address conspiracy theory detection as proposed by the ACTI @ EVALITA
2023 shared task. The combination of pre-trained sentence Transformer models
and data augmentation techniques enabled us to secure first place in the final
leaderboard of both sub-tasks. Our methodology attained F1 scores of 85.71% in
the binary classification and 91.23% for the fine-grained conspiracy topic
classification, surpassing other competing systems
L'analyse de la complexité du discours et du texte pour apprendre et collaborer
With the advent and increasing popularity of Computer Supported Collaborative Learning (CSCL) and e-learning technologies, the need of automatic assessment and of teacher/tutor support for the two tightly intertwined activities of comprehension of reading materials and of collaboration among peers has grown significantly. Whereas a shallow or surface analysis is easily achievable, a deeper understanding of the discourse is required, extended by meta-cognitive information available from multiple sources as self-explanations. In this context, we use a polyphonic model of discourse derived from Bakhtinâs work as a paradigm for analyzing CSCL conversations, as well as cohesion graph building designed for creating an underlying discourse structure. This enables us to address both general texts and conversations and to incorporate comprehension and collaboration specific activities in a unique framework. As specificity of the analysis, in terms of individual learning we have focused on the identification of reading strategies and on providing a multi-dimensional textual complexity model integrating surface, word specific, morphology, syntax and semantic factors. Complementarily, the collaborative learning dimension is centered on the evaluation of participantsâ involvement, as well as on collaboration assessment through the use of two computational models: a polyphonic model, defined in terms of voice inter-animation, and a specific social knowledge-building model, derived from the specially designed cohesion graph corroborated with a proposed utterance scoring mechanism. Our approach integrates advanced Natural Language Processing techniques and is focused on providing a qualitative estimation of the learning process. Therefore, two tightly coupled perspectives are taken into consideration: comprehension on one hand is centered on knowledge-building, self-explanations from which multiple reading strategies can be identified, whereas collaboration, on the other, can be seen as social involvement, ideas or voices generation, intertwining and inter-animation in a given context. Various cognitive validations for all our automated evaluation systems have been conducted and scenarios including the use of ReaderBench, our most advanced system, in different educational contexts have been built. One of the most important goals of our model is to enhance understanding as a âmediator of learningâ by providing automated feedback to both learners and teachers or tutors. The main benefits are its flexibility, extensibility and nevertheless specificity for covering multiple stages, starting from reading classroom materials, to discussing on specific topics in a collaborative manner, and finishing the feedback loop by verbalizing metacognitive thoughts in order to obtain a clear perspective over oneâs comprehension level and appropriate feedback about the collaborative learning processes.Lâapprentissage collaboratif assistĂ© par ordinateur et les technologies dâe-learning devenant de plus en plus populaires et intĂ©grĂ©s dans des contextes Ă©ducatifs, le besoin se fait sentir de disposer dâoutils dâĂ©valuation automatique et dâaide aux enseignants ou tuteurs pour les deux activitĂ©s, fortement couplĂ©es, de comprĂ©hension de textes et collaboration entre pairs. Bien quâune analyse de surface de ces activitĂ©s est aisĂ©ment rĂ©alisable, une comprĂ©hension plus profonde et complĂšte du discours en jeu est nĂ©cessaire, complĂ©tĂ©e par une analyse de lâinformation mĂ©ta-cognitive disponible par diverses sources, comme par exemples les auto-explications des apprenants. Dans ce contexte, nous utilisons un modĂšle dialogique issu des travaux de Bakhtine pour analyser les conversations collaboratives, et une approche thĂ©orique visant Ă unifier les activitĂ©s de comprĂ©hension et de collaboration dans un mĂȘme cadre, utilisant la construction de graphes de cohĂ©sion. Plus spĂ©cifiquement, nous nous sommes centrĂ©s sur la dimension individuelle de lâapprentissage, analysĂ©e Ă partir de lâidentification de stratĂ©gies de lecture et sur la mise au jour dâun modĂšle de la complexitĂ© textuelle intĂ©grant des facteurs de surface, lexicaux, morphologiques, syntaxiques et sĂ©mantiques. En complĂ©ment, la dimension collaborative de lâapprentissage est centrĂ©e sur lâĂ©valuation de lâimplication des participants, ainsi que sur lâĂ©valuation de leur collaboration par deux modĂšles computationnels: un modĂšle polyphonique, dĂ©fini comme lâinter-animation de voix selon de multiples perspectives, un modĂšle spĂ©cifique de construction sociale de connaissances, fondĂ© sur le graphe de cohĂ©sion et un mĂ©canisme dâĂ©valuation des tours de parole. Notre approche met en Ćuvre des techniques avancĂ©es de traitement automatique de la langue et a pour but de formaliser une Ă©valuation qualitative du processus dâapprentissage. Ainsi, deux perspectives fortement interreliĂ©es sont prises en considĂ©ration : dâune part, la comprĂ©hension, centrĂ©e sur la construction de connaissances et les auto-explications Ă partir desquelles les stratĂ©gies de lecture sont identifiĂ©es ; dâautre part la collaboration, qui peut ĂȘtre dĂ©finie comme lâimplication sociale, la gĂ©nĂ©ration dâidĂ©es ou de voix en interanimation dans un contexte donnĂ©. Des validations cognitives de nos diffĂ©rents systĂšmes dâĂ©valuation automatique ont Ă©tĂ© rĂ©alisĂ©es, et nous avons conçu des scĂ©narios dâutilisation de ReaderBench, notre systĂšme le plus avancĂ©, dans diffĂ©rents contextes dâenseignement. Lâun des buts principaux de notre modĂšle est de favoriser la comprĂ©hension vue en tant que « mĂ©diatrice de lâapprentissage », en procurant des rĂ©troactions automatiques aux apprenants et enseignants ou tuteurs. Leur avantage est triple: leur flexibilitĂ©, leur extensibilitĂ© et, cependant, leur spĂ©cificitĂ©, car ils couvrent de multiples Ă©tapes de lâactivitĂ© dâapprentissage, de la lecture de matĂ©riel dâapprentissage Ă lâĂ©criture de synthĂšses de cours en passant par la discussion collaborative de contenus de cours et la verbalisation mĂ©tacognitive de jugements de comprĂ©hension, afin dâobtenir une perspective complĂšte du niveau de comprĂ©hension et de gĂ©nĂ©rer des rĂ©troactions appropriĂ©es sur le processus dâapprentissage collaboratif
Voices' inter-animation detection with ReaderBench. Modelling and assessing polyphony in CSCL chats as voice synergy
International audienceStarting from dialogism in which every act is perceived as a dialogue, we shift the perspective towards multi-participant chat conversations from Computer Supported Collaborative Learning in which ideas, points of view or more generally put voices interact, inter-animate and generate the context of a conversation. Within this perspective of discourse analysis, we introduce an implemented framework, ReaderBench, for modeling and automatically evaluating polyphony that emerges as an overlap or synergy of voices. Moreover, multiple evaluation factors were analyzed for quantifying the importance of a voice and various functions were experimented to best reflect the synergic effect of co- occurring voices for modeling the underlying discourse structure
Utterances Assessment in Chat Conversations
International audienceWith the continuous evolution of collaborative environments, the needs of automatic analyses and assessment of participants in instant messenger conferences (chat) have become essential. For these aims, on one hand, a series of factors based on natural language processing (including lexical analysis and Latent Semantic Analysis) and data-mining have been taken into consideration. On the other hand, in order to thoroughly assess participants, measures as Page's essay grading, readability and social networks analysis metrics were computed. The weights of each factor in the overall grading system are optimized using a genetic algorithm whose entries are provided by a perceptron in order to ensure numerical stability. A gold standard has been used for evaluating the system's performance
Voices' inter-animation detection with ReaderBench. Modelling and assessing polyphony in CSCL chats as voice synergy
International audienceStarting from dialogism in which every act is perceived as a dialogue, we shift the perspective towards multi-participant chat conversations from Computer Supported Collaborative Learning in which ideas, points of view or more generally put voices interact, inter-animate and generate the context of a conversation. Within this perspective of discourse analysis, we introduce an implemented framework, ReaderBench, for modeling and automatically evaluating polyphony that emerges as an overlap or synergy of voices. Moreover, multiple evaluation factors were analyzed for quantifying the importance of a voice and various functions were experimented to best reflect the synergic effect of co- occurring voices for modeling the underlying discourse structure
Utterances Assessment in Chat Conversations
International audienceWith the continuous evolution of collaborative environments, the needs of automatic analyses and assessment of participants in instant messenger conferences (chat) have become essential. For these aims, on one hand, a series of factors based on natural language processing (including lexical analysis and Latent Semantic Analysis) and data-mining have been taken into consideration. On the other hand, in order to thoroughly assess participants, measures as Page's essay grading, readability and social networks analysis metrics were computed. The weights of each factor in the overall grading system are optimized using a genetic algorithm whose entries are provided by a perceptron in order to ensure numerical stability. A gold standard has been used for evaluating the system's performance
Framework and Implications of Virtual Neurorobotics
Despite decades of societal investment in artificial learning systems, truly âintelligentâ systems have yet to be realized. These traditional models are based on input-output pattern optimization and/or cognitive production rule modeling. One response has been social robotics, using the interaction of human and robot to capture important cognitive dynamics such as cooperation and emotion; to date, these systems still incorporate traditional learning algorithms. More recently, investigators are focusing on the core assumptions of the brain âalgorithmâ itselfâtrying to replicate uniquely âneuromorphicâ dynamics such as action potential spiking and synaptic learning. Only now are large-scale neuromorphic models becoming feasible, due to the availability of powerful supercomputers and an expanding supply of parameters derived from research into the brain's interdependent electrophysiological, metabolomic and genomic networks. Personal computer technology has also led to the acceptance of computer-generated humanoid images, or âavatarsâ, to represent intelligent actors in virtual realities. In a recent paper, we proposed a method of virtual neurorobotics (VNR) in which the approaches above (social-emotional robotics, neuromorphic brain architectures, and virtual reality projection) are hybridized to rapidly forward-engineer and develop increasingly complex, intrinsically intelligent systems. In this paper, we synthesize our research and related work in the field and provide a framework for VNR, with wider implications for research and practical applications
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