The Web We Mix: Benevolent AIs for a Resilient Web

International audienceIn this keynote abstract I mention a number of works from the research team Wimmics (pronounced "we mix") that has been studying the challenges in bridging social semantics and formal semantics on the Web. These contributions address some of the challenges in connecting AIs to the Web

Multi-Task Attentive Residual Networks for Argument Mining

We explore the use of residual networks and neural attention for argument mining and in particular link prediction. The method we propose makes no assumptions on document or argument structure. We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble. We evaluate it on a challenging data set consisting of user-generated comments, as well as on two other datasets consisting of scientific publications. On the user-generated content dataset, our model outperforms state-of-the-art methods that rely on domain knowledge. On the scientific literature datasets it achieves results comparable to those yielded by BERT-based approaches but with a much smaller model size.Comment: 12 pages, 2 figures, submitted to IEEE Transactions on Neural Networks and Learning System

Evidence Type Classification in Randomized Controlled Trials

International audienceRandomized Controlled Trials (RCT) are a common type of experimental studies in the medical domain for evidence-based decision making. The ability to automatically extract the arguments proposed therein can be of valuable support for clinicians and practitioners in their daily evidence-based decision making activities. Given the peculiarity of the medical domain and the required level of detail, standard approaches to argument component detection in argument(ation) mining are not fine-grained enough to support such activities. In this paper, we introduce a new sub-task of the argument component identification task: evidence type classification. To address it, we propose a supervised approach and we test it on a set of RCT abstracts on different medical topics

Yes, we can! Mining Arguments in 50 Years of US Presidential Campaign Debates

International audiencePolitical debates offer a rare opportunity for citizens to compare the candidates’ positions on the most controversial topics of the campaign. Thus they represent a natural application scenario for Argument Mining. As existing research lacks solid empirical investigation of the typology of argument components in political debates, we fill this gap by proposing an Argument Mining approach to political debates. We address this task in an empirical manner by annotating 39 political debates from the last 50 years of US presidential campaigns, creating a new corpus of 29k argument components, labeled as premises and claims. We then propose two tasks: (1) identifying the argumentative components in such debates, and (2) classifying them as premises and claims. We show that feature-rich SVM learners and Neural Network architectures outperform standard baselines in Argument Mining over such complex data. We release the new corpus USElecDeb60To16 and the accompanying software under free licenses to the research community

Transformer-based Argument Mining for Healthcare Applications

International audienceArgument(ation) Mining (AM) typically aims at identifying argumentative components in text and predicting the relations among them. Evidence-based decision making in the health-care domain targets at supporting clinicians in their deliberation process to establish the best course of action for the case under evaluation. Although the reasoning stage of this kind of frameworks received considerable attention, little effort has been devoted to the mining stage. We extended an existing dataset by annotating 500 abstracts of Randomized Controlled Trials (RCT) from the MEDLINE database, leading to a dataset of 4198 argument components and 2601 argument relations on different diseases (i.e., neoplasm, glau-coma, hepatitis, diabetes, hypertension). We propose a complete argument mining pipeline for RCTs, classifying argument components as evidence and claims, and predicting the relation, i.e., attack or support , holding between those argument components. We experiment with deep bidirectional transformers in combination with different neural architectures (i.e., LSTM, GRU and CRF) and obtain a macro F1-score of .87 for component detection and .68 for relation prediction , outperforming current state-of-the-art end-to-end AM systems

Argument mining with graph representation learning

Argument Mining (AM) is a unique task in Natural Language Processing (NLP) that targets arguments: a meaningful logical structure in human language. Since the argument plays a significant role in the legal field, the interdisciplinary study of AM on legal texts has significant promise. For years, a pipeline architecture has been used as the standard paradigm in this area. Although this simplifies the development and management of AM systems, the connection between different parts of the pipeline causes inevitable shortcomings such as cascading error propagation. This paper presents an alternative perspective of the AM task, whereby legal documents are represented as graph structures and the AM task is undertaken as a hybrid approach incorporating Graph Neural Networks (GNNs), graph augmentation and collective classification. GNNs have been demonstrated to be an effective method for representation learning on graphs, and they have been successfully applied to many other NLP tasks. In contrast to previous pipeline-based architecture, our approach results in a single end-to-end classifier for the identification and classification of argumentative text segments. Experiments based on corpora from both the European Court of Human Rights (ECHR) and the Court of Jus- tice of the European Union (CJEU) show that our approach achieves strong results compared to state-of-the-art baselines. Both the graph augmentation and collective classification steps are shown to improve performance on both datasets when compared to using GNNs alone

Argumentation dialogues in web-based GDSS: an approach using machine learning techniques

Tese de doutoramento em InformaticsA tomada de decisão está presente no dia a dia de qualquer pessoa, mesmo que muitas vezes ela não tenha consciência disso. As decisões podem estar relacionadas com problemas quotidianos, ou podem estar relacionadas com questões mais complexas, como é o caso das questões organizacionais. Normalmente, no contexto organizacional, as decisões são tomadas em grupo. Os Sistemas de Apoio à Decisão em Grupo têm sido estudados ao longo das últimas décadas com o objetivo de melhorar o apoio prestado aos decisores nas mais diversas situações e/ou problemas a resolver. Existem duas abordagens principais à implementação de Sistemas de Apoio à Decisão em Grupo: a abordagem clássica, baseada na agregação matemática das preferências dos diferentes elementos do grupo e as abordagens baseadas na negociação automática (e.g. Teoria dos Jogos, Argumentação, entre outras). Os atuais Sistemas de Apoio à Decisão em Grupo baseados em argumentação podem gerar uma enorme quantidade de dados. O objetivo deste trabalho de investigação é estudar e desenvolver modelos utilizando técnicas de aprendizagem automática para extrair conhecimento dos diálogos argumentativos realizados pelos decisores, mais concretamente, pretende-se criar modelos para analisar, classificar e processar esses dados, potencializando a geração de novo conhecimento que será utilizado tanto por agentes inteligentes, como por decisiores reais. Promovendo desta forma a obtenção de consenso entre os membros do grupo. Com base no estudo da literatura e nos desafios em aberto neste domínio, formulou-se a seguinte hipótese de investigação - É possível usar técnicas de aprendizagem automática para apoiar diálogos argumentativos em Sistemas de Apoio à Decisão em Grupo baseados na web. No âmbito dos trabalhos desenvolvidos, foram aplicados algoritmos de classificação supervisionados a um conjunto de dados contendo argumentos extraídos de debates online, criando um classificador de frases argumentativas que pode classificar automaticamente (A favor/Contra) frases argumentativas trocadas no contexto da tomada de decisão. Foi desenvolvido um modelo de clustering dinâmico para organizar as conversas com base nos argumentos utilizados. Além disso, foi proposto um Sistema de Apoio à Decisão em Grupo baseado na web que possibilita apoiar grupos de decisores independentemente de sua localização geográfica. O sistema permite a criação de problemas multicritério e a configuração das preferências, intenções e interesses de cada decisor. Este sistema de apoio à decisão baseado na web inclui os dashboards de relatórios inteligentes que são gerados através dos resultados dos trabalhos alcançados pelos modelos anteriores já referidos. A concretização de cada um dos objetivos permitiu validar as questões de investigação identificadas e assim responder positivamente à hipótese definida.Decision-making is present in anyone’s daily life, even if they are often unaware of it. Decisions can be related to everyday problems, or they can be related to more complex issues, such as organizational issues. Normally, in the organizational context, decisions are made in groups. Group Decision Support Systems have been studied over the past decades with the aim of improving the support provided to decision-makers in the most diverse situations and/or problems to be solved. There are two main approaches to implementing Group Decision Support Systems: the classical approach, based on the mathematical aggregation of the preferences of the different elements of the group, and the approaches based on automatic negotiation (e.g. Game Theory, Argumentation, among others). Current argumentation-based Group Decision Support Systems can generate an enormous amount of data. The objective of this research work is to study and develop models using automatic learning techniques to extract knowledge from argumentative dialogues carried out by decision-makers, more specifically, it is intended to create models to analyze, classify and process these data, enhancing the generation of new knowledge that will be used both by intelligent agents and by real decision-makers. Promoting in this way the achievement of consensus among the members of the group. Based on the literature study and the open challenges in this domain, the following research hypothesis was formulated - It is possible to use machine learning techniques to support argumentative dialogues in web-based Group Decision Support Systems. As part of the work developed, supervised classification algorithms were applied to a data set containing arguments extracted from online debates, creating an argumentative sentence classifier that can automatically classify (For/Against) argumentative sentences exchanged in the context of decision-making. A dynamic clustering model was developed to organize conversations based on the arguments used. In addition, a web-based Group Decision Support System was proposed that makes it possible to support groups of decision-makers regardless of their geographic location. The system allows the creation of multicriteria problems and the configuration of preferences, intentions, and interests of each decision-maker. This web-based decision support system includes dashboards of intelligent reports that are generated through the results of the work achieved by the previous models already mentioned. The achievement of each objective allowed validation of the identified research questions and thus responded positively to the defined hypothesis.I also thank to Fundação para a Ciência e a Tecnologia, for the Ph.D. grant funding with the reference: SFRH/BD/137150/2018

Computational Argumentation for the Automatic Analysis of Argumentative Discourse and Human Persuasion

Tesis por compendio[ES] La argumentación computacional es el área de investigación que estudia y analiza el uso de distintas técnicas y algoritmos que aproximan el razonamiento argumentativo humano desde un punto de vista computacional. En esta tesis doctoral se estudia el uso de distintas técnicas propuestas bajo el marco de la argumentación computacional para realizar un análisis automático del discurso argumentativo, y para desarrollar técnicas de persuasión computacional basadas en argumentos. Con estos objetivos, en primer lugar se presenta una completa revisión del estado del arte y se propone una clasificación de los trabajos existentes en el área de la argumentación computacional. Esta revisión nos permite contextualizar y entender la investigación previa de forma más clara desde la perspectiva humana del razonamiento argumentativo, así como identificar las principales limitaciones y futuras tendencias de la investigación realizada en argumentación computacional. En segundo lugar, con el objetivo de solucionar algunas de estas limitaciones, se ha creado y descrito un nuevo conjunto de datos que permite abordar nuevos retos y investigar problemas previamente inabordables (e.g., evaluación automática de debates orales). Conjuntamente con estos datos, se propone un nuevo sistema para la extracción automática de argumentos y se realiza el análisis comparativo de distintas técnicas para esta misma tarea. Además, se propone un nuevo algoritmo para la evaluación automática de debates argumentativos y se prueba con debates humanos reales. Finalmente, en tercer lugar se presentan una serie de estudios y propuestas para mejorar la capacidad persuasiva de sistemas de argumentación computacionales en la interacción con usuarios humanos. De esta forma, en esta tesis se presentan avances en cada una de las partes principales del proceso de argumentación computacional (i.e., extracción automática de argumentos, representación del conocimiento y razonamiento basados en argumentos, e interacción humano-computador basada en argumentos), así como se proponen algunos de los cimientos esenciales para el análisis automático completo de discursos argumentativos en lenguaje natural.[CA] L'argumentació computacional és l'àrea de recerca que estudia i analitza l'ús de distintes tècniques i algoritmes que aproximen el raonament argumentatiu humà des d'un punt de vista computacional. En aquesta tesi doctoral s'estudia l'ús de distintes tècniques proposades sota el marc de l'argumentació computacional per a realitzar una anàlisi automàtic del discurs argumentatiu, i per a desenvolupar tècniques de persuasió computacional basades en arguments. Amb aquestos objectius, en primer lloc es presenta una completa revisió de l'estat de l'art i es proposa una classificació dels treballs existents en l'àrea de l'argumentació computacional. Aquesta revisió permet contextualitzar i entendre la investigació previa de forma més clara des de la perspectiva humana del raonament argumentatiu, així com identificar les principals limitacions i futures tendències de la investigació realitzada en argumentació computacional. En segon lloc, amb l'objectiu de sol$\cdot$lucionar algunes d'aquestes limitacions, hem creat i descrit un nou conjunt de dades que ens permet abordar nous reptes i investigar problemes prèviament inabordables (e.g., avaluació automàtica de debats orals). Conjuntament amb aquestes dades, es proposa un nou sistema per a l'extracció d'arguments i es realitza l'anàlisi comparativa de distintes tècniques per a aquesta mateixa tasca. A més a més, es proposa un nou algoritme per a l'avaluació automàtica de debats argumentatius i es prova amb debats humans reals. Finalment, en tercer lloc es presenten una sèrie d'estudis i propostes per a millorar la capacitat persuasiva de sistemes d'argumentació computacionals en la interacció amb usuaris humans. D'aquesta forma, en aquesta tesi es presenten avanços en cada una de les parts principals del procés d'argumentació computacional (i.e., l'extracció automàtica d'arguments, la representació del coneixement i raonament basats en arguments, i la interacció humà-computador basada en arguments), així com es proposen alguns dels fonaments essencials per a l'anàlisi automàtica completa de discursos argumentatius en llenguatge natural.[EN] Computational argumentation is the area of research that studies and analyses the use of different techniques and algorithms that approximate human argumentative reasoning from a computational viewpoint. In this doctoral thesis we study the use of different techniques proposed under the framework of computational argumentation to perform an automatic analysis of argumentative discourse, and to develop argument-based computational persuasion techniques. With these objectives in mind, we first present a complete review of the state of the art and propose a classification of existing works in the area of computational argumentation. This review allows us to contextualise and understand the previous research more clearly from the human perspective of argumentative reasoning, and to identify the main limitations and future trends of the research done in computational argumentation. Secondly, to overcome some of these limitations, we create and describe a new corpus that allows us to address new challenges and investigate on previously unexplored problems (e.g., automatic evaluation of spoken debates). In conjunction with this data, a new system for argument mining is proposed and a comparative analysis of different techniques for this same task is carried out. In addition, we propose a new algorithm for the automatic evaluation of argumentative debates and we evaluate it with real human debates. Thirdly, a series of studies and proposals are presented to improve the persuasiveness of computational argumentation systems in the interaction with human users. In this way, this thesis presents advances in each of the main parts of the computational argumentation process (i.e., argument mining, argument-based knowledge representation and reasoning, and argument-based human-computer interaction), and proposes some of the essential foundations for the complete automatic analysis of natural language argumentative discourses.This thesis has been partially supported by the Generalitat Valenciana project PROME- TEO/2018/002 and by the Spanish Government projects TIN2017-89156-R and PID2020- 113416RB-I00.Ruiz Dolz, R. (2023). Computational Argumentation for the Automatic Analysis of Argumentative Discourse and Human Persuasion [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/194806Compendi

Argument Mining on Clinical Trials

Ces dernières années, le domaine de la e-santé a vu un intérêt croissant pour la définition de systèmes intelligents ayant le but d'accompagner les cliniciens dans leurs tâches et leurs activités quotidiennes. D’ailleurs, cela inclut de nouveaux systèmes pour le domaine de la médecine basée sur les preuves. Ce dernier repose sur le principe de l'évaluation critique des preuves médicales et de la combinaison de ces preuves de haute qualité avec l'expérience clinique individuelle du praticien par rapport à la situation d'un patient pour obtenir le meilleur résultat possible. La plupart des systèmes intelligents proposés visent soit à extraire des informations sur la qualité des preuves issues des essais cliniques, de directives cliniques ou des dossiers de santé électroniques, soit à aider dans les processus de prise de décision, sur la base de cadres de raisonnement. Le travail de cette thèse va au-delà de l'état de l'art des systèmes d'extraction d'informations actuellement proposés dans ce contexte. Il utilise des méthodes d'analyse d'arguments pour extraire et classifier les composants d’argumentation (c'est-à-dire les preuves et les conclusions d'un essai clinique) et leurs relations (c'est-à-dire le support et l'attaque). Un cadre de fouille d’arguments (Argument Mining) est proposé et amélioré pour intégrer des informations supplémentaires inspirées par les cadres biomédicaux courants pour l'analyse des essais cliniques. Ces extensions comprennent la détection des éléments PICO et un module d'analyse des résultats pour identifier et classer les effets (c'est-à-dire améliorés, augmentés, diminués, pas de différence, pas d'occurrence) d'une intervention sur le résultat de l'essai. Dans ce contexte, un jeu de données, composé de 660 résumés d'essais cliniques dans la base de données MEDLINE, a été annoté, en résultant dans le construction d’un jeu de données étiquetées qui inclut 4198 composants d’argumentation, 2601 relations d'argumentation et 3351 résultats d’intervention sur cinq maladies différentes (néoplasme, glaucome, hépatite, diabète, hypertension). Diverses approches d'apprentissage automatique et profond allant des SVM aux architectures récentes basées sur les réseaux de neurones ont été expérimentées, obtenant un F1 macro de 0,87 pour la détection de composants d'argumentation et de 0,68 pour la prédiction des relation d’argumentation, surpassant les résultats obtenus pas les systèmes de detection d'arguments dans l’état de l’art. De plus, une demo d’un système, appelé ACTA, a été développée pour démontrer l'utilisation pratique de l'approche basée sur les arguments développée pour analyser les essais cliniques. Ce système de démonstration a été intégré dans le contexte du projet Covid-on-the-Web pour créer des données liées riches et exploitables sur le Covid-19.In the latest years, the healthcare domain has seen an increasing interest in the definition of intelligent systems to support clinicians in their everyday tasks and activities. Among others, this includes novel systems for the field of Evidence-based Medicine. The latter relies on the principle of critically appraising medical evidence and combining high quality evidence with the individual clinical experience of the practitioner with respect to the circumstances of a patient to achieve the best possible outcome. Hence, most of the proposed intelligent systems aim either at extracting information concerning the quality of evidence from clinical trials, clinical guidelines, or electronic health records, or assist in the decision making processes, based on reasoning frameworks. The work in this thesis goes beyond the state-of-the-art of currently proposed information extraction systems. It employs Argument Mining methods to extract and classify argumentative components (i.e., evidence and claims of a clinical trial) and their relations (i.e., support, attack). An Argument Mining pipeline is proposed and further enhanced to integrate additional information inspired by prevalent biomedical frameworks for the analysis of clinical trials. These extensions comprise the detection of PICO elements and an outcome analysis module to identify and classify the effects (i.e., improved, increased, decreased, no difference, no occurrence) of an intervention on the outcome of the trial. In this context, a dataset, composed of 660 Randomized Controlled Trial abstracts from the MEDLINE database, was annotated, leading to a labeled dataset with 4198 argument components, 2601 argument relations, and 3351 outcomes on five different diseases (i.e., neoplasm, glaucoma, hepatitis, diabetes, hypertension). Various Machine Learning approaches ranging from feature-based SVMs to recent neural architectures have been experimented with, where deep bidirectional transformers obtain a macro F1-score of .87 for argument component detection and .68 for argument relation prediction, outperforming current state-of-the-art Argument Mining systems. Additionally, a Proof-of-Concept system, called ACTA, was developed to demonstrate the practical use of the developed argument-based approach to analyse clinical trials. This demo system was further integrated in the context of the Covid-on-the-Web project to create rich and actionable Linked Data about the Covid-19