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  • Journal article
    Cocarascu O, Toni F, 2018,

    Combining deep learning and argumentative reasoning for the analysis of social media textual content using small datasets

    , Computational Linguistics, Vol: 44, Pages: 833-858, ISSN: 0891-2017

    The use of social media has become a regular habit for many and has changed the way people interact with each other. In this article, we focus on analysing whether news headlines support tweets and whether reviews are deceptive by analysing the interaction or the influence that these texts have on the others, thus exploiting contextual information. Concretely, we define a deep learning method for Relation-based Argument Mining to extract argumentative relations of attack and support. We then use this method for determining whether news articles support tweets, a useful task in fact-checking settings, where determining agreement towards a statement is a useful step towards determining its truthfulness. Furthermore we use our method for extracting Bipolar Argumentation Frameworks from reviews to help detect whether they are deceptive. We show experimentally that our method performs well in both settings. In particular, in the case of deception detection, our method contributes a novel argumentative feature that, when used in combination with other features in standard supervised classifiers, outperforms the latter even on small datasets.

  • Conference paper
    Popescu C, Cocarascu O, Toni F, 2018,

    A platform for crowdsourcing corpora for argumentative

    , The International Workshop on Dialogue, Explanation and Argumentation in Human-Agent Interaction (DEXAHAI)

    One problem that Argument Mining (AM) is facing is the difficultyof obtaining suitable annotated corpora. We propose a web-basedplatform, BookSafari, that allows crowdsourcing of annotated cor-pora forrelation-based AMfrom users providing reviews for booksand exchanging opinions about these reviews to facilitate argumen-tative dialogue. The annotations amount to pairwise argumentativerelations ofattackandsupportbetween opinions and between opin-ions and reviews. As a result of the annotations, reviews and opinionsform structured debates which can be understood as bipolar argu-mentation frameworks. The platform also empowers annotationsof the same pairs by multiple annotators and can support differentmeasures of inter-annotator agreement and corpora selection.

  • Conference paper
    Cyras K, Delaney B, Prociuk D, Toni F, Chapman M, Dominguez J, Curcin Vet al., 2018,

    Argumentation for explainable reasoning with conflicting medical recommendations

    , Reasoning with Ambiguous and Conflicting Evidence and Recommendations in Medicine (MedRACER 2018), Pages: 14-22

    Designing a treatment path for a patient suffering from mul-tiple conditions involves merging and applying multiple clin-ical guidelines and is recognised as a difficult task. This isespecially relevant in the treatment of patients with multiplechronic diseases, such as chronic obstructive pulmonary dis-ease, because of the high risk of any treatment change havingpotentially lethal exacerbations. Clinical guidelines are typi-cally designed to assist a clinician in treating a single condi-tion with no general method for integrating them. Addition-ally, guidelines for different conditions may contain mutuallyconflicting recommendations with certain actions potentiallyleading to adverse effects. Finally, individual patient prefer-ences need to be respected when making decisions.In this work we present a description of an integrated frame-work and a system to execute conflicting clinical guidelinerecommendations by taking into account patient specific in-formation and preferences of various parties. Overall, ourframework combines a patient’s electronic health record datawith clinical guideline representation to obtain personalisedrecommendations, uses computational argumentation tech-niques to resolve conflicts among recommendations while re-specting preferences of various parties involved, if any, andyields conflict-free recommendations that are inspectable andexplainable. The system implementing our framework willallow for continuous learning by taking feedback from thedecision makers and integrating it within its pipeline.

  • Conference paper
    Baroni P, Borsato S, Rago A, Toni Fet al., 2018,

    The "Games of Argumentation" web platform

    , 7th International Conference on Computational Models of Argument (COMMA 2018), Publisher: IOS Press, Pages: 447-448, ISSN: 0922-6389

    This demo presents the web system “Games of Argumentation”, which allows users to build argumentation graphs and examine them in a game-theoretical manner using up to three different evaluation techniques. The concurrent evaluations of arguments using different techniques, which may be qualitative or quantitative, provides a significant aid to users in both understanding game-theoretical argumentation semantics and pinpointing their differences from alternative semantics, traditional or otherwise, to differentiate between them.

  • Conference paper
    Rago A, Baroni P, Toni F, 2018,

    On instantiating generalised properties of gradual argumentation frameworks

    , SUM 2018, Publisher: Springer Verlag, Pages: 243-259, ISSN: 0302-9743

    Several gradual semantics for abstract and bipolar argumentation have been proposed in the literature, ascribing to each argument a value taken from a scale, i.e. an ordered set. These values somewhat match the arguments’ dialectical status and provide an indication of their dialectical strength, in the context of the given argumentation framework. These research efforts have been complemented by formulations of several properties that these gradual semantics may satisfy. More recently a synthesis of many literature properties into more general groupings based on parametric definitions has been proposed. In this paper we show how this generalised parametric formulation enables the identification of new properties not previously considered in the literature and discuss their usefulness to capture alternative requirements coming from different application contexts.

  • Conference paper
    Rago A, Cocarascu O, Toni F, 2018,

    Argumentation-based recommendations: fantastic explanations and how to find them

    , The Twenty-Seventh International Joint Conference on Artificial Intelligence, (IJCAI 2018), Pages: 1949-1955

    A significant problem of recommender systems is their inability to explain recommendations, resulting in turn in ineffective feedback from users and the inability to adapt to users’ preferences. We propose a hybrid method for calculating predicted ratings, built upon an item/aspect-based graph with users’ partially given ratings, that can be naturally used to provide explanations for recommendations, extracted from user-tailored Tripolar Argumentation Frameworks (TFs). We show that our method can be understood as a gradual semantics for TFs, exhibiting a desirable, albeit weak, property of balance. We also show experimentally that our method is competitive in generating correct predictions, compared with state-of-the-art methods, and illustrate how users can interact with the generated explanations to improve quality of recommendations.

  • Conference paper
    Cocarascu O, Cyras K, Toni F, 2018,

    Explanatory predictions with artificial neural networks and argumentation

    , Workshop on Explainable Artificial Intelligence (XAI)

    Data-centric AI has proven successful in severaldomains, but its outputs are often hard to explain.We present an architecture combining ArtificialNeural Networks (ANNs) for feature selection andan instance of Abstract Argumentation (AA) forreasoning to provide effective predictions, explain-able both dialectically and logically. In particular,we train an autoencoder to rank features in input ex-amples, and select highest-ranked features to gen-erate an AA framework that can be used for mak-ing and explaining predictions as well as mappedonto logical rules, which can equivalently be usedfor making predictions and for explaining.Weshow empirically that our method significantly out-performs ANNs and a decision-tree-based methodfrom which logical rules can also be extracted.

  • Conference paper
    Baroni P, Rago A, Toni F, 2018,

    How many Properties do we need for Gradual Argumentation?

    , AAAI 2018, Publisher: AAAI

    The study of properties of gradual evaluation methods inargumentation has received increasing attention in recentyears, with studies devoted to various classes of frame-works/methods leading to conceptually similar but formallydistinct properties in different contexts. In this paper we pro-vide a systematic analysis for this research landscape by mak-ing three main contributions. First, we identify groups of con-ceptually related properties in the literature, which can be re-garded as based on common patterns and, using these pat-terns, we evidence that many further properties can be consid-ered. Then, we provide a simplifying and unifying perspec-tive for these properties by showing that they are all impliedby the parametric principles of (either strict or non-strict) bal-ance and monotonicity. Finally, we show that (instances of)these principles are satisfied by several quantitative argumen-tation formalisms in the literature, thus confirming their gen-eral validity and their utility to support a compact, yet com-prehensive, analysis of properties of gradual argumentation.

  • Conference paper
    Baroni P, Borsato S, Rago A, Toni Fet al., 2018,

    The "Games of Argumentation" Web Platform.

    , Publisher: IOS Press, Pages: 447-448
  • Conference paper
    Baroni P, Comini G, Rago A, Toni Fet al., 2017,

    Abstract Games of Argumentation Strategy and Game-Theoretical Argument Strength

    , PRIMA, Publisher: Springer, Pages: 403-419, ISSN: 0302-9743

    We define a generic notion of abstract games of argumentation strategy for (attack-only and bipolar) argumentation frameworks, which are zero-sum games whereby two players put forward sets of arguments and get a reward for their combined choices. The value of these games, in the classical game-theoretic sense, can be used to define measures of (quantitative) game-theoretic strength of arguments, which are different depending on whether either or both players have an “agenda” (i.e. an argument they want to be accepted). We show that this general scheme captures as a special instance a previous proposal in the literature (single agenda, attack-only frameworks), and seamlessly supports the definition of a spectrum of novel measures of game-theoretic strength where both players have an agenda and/or bipolar frameworks are considered. We then discuss the applicability of these instances of game-theoretic strength in different contexts and analyse their basic properties.

  • Conference paper
    Rago A, Toni F, 2017,

    Quantitative Argumentation Debates with Votes for Opinion Polling

    , PRIMA, Publisher: Springer, Pages: 369-385, ISSN: 0302-9743

    Opinion polls are used in a variety of settings to assess the opinions of a population, but they mostly conceal the reasoning behind these opinions. Argumentation, as understood in AI, can be used to evaluate opinions in dialectical exchanges, transparently articulating the reasoning behind the opinions. We give a method integrating argumentation within opinion polling to empower voters to add new statements that render their opinions in the polls individually rational while at the same time justifying them. We then show how these poll results can be amalgamated to give a collectively rational set of voters in an argumentation framework. Our method relies upon Quantitative Argumentation Debate for Voting (QuAD-V) frameworks, which extend QuAD frameworks (a form of bipolar argumentation frameworks in which arguments have an intrinsic strength) with votes expressing individuals’ opinions on arguments.

  • Conference paper
    Bao Z, Cyras K, Toni F, 2017,

    ABAplus: Attack Reversal in Abstract and Structured Argumentation with Preferences

    , PRIMA 2017: The 20th International Conference on Principles and Practice of Multi-Agent Systems, Publisher: Springer Verlag, ISSN: 0302-9743

    We present ABAplus, a system that implements reasoningwith the argumentation formalism ABA+. ABA+ is a structured argumentationformalism that extends Assumption-Based Argumentation(ABA) with preferences and accounts for preferences via attack reversal.ABA+ also admits as instance Preference-based Argumentation whichaccounts for preferences by reversing attacks in abstract argumentation(AA). ABAplus readily implements attack reversal in both AA and ABAstylestructured argumentation. ABAplus affords computation, visualisationand comparison of extensions under five argumentation semantics.It is available both as a stand-alone system and as a web application.

  • Conference paper
    Cyras K, Schulz C, Toni F, 2017,

    Capturing Bipolar Argumentation in Non-flat Assumption-Based Argumentation

    , PRIMA 2017: Principles and Practice of Multi-Agent Systems - 20th International Conference, Publisher: Springer Verlag, Pages: 386-402, ISSN: 0302-9743

    Bipolar Argumentation Frameworks (BAFs) encompass both attacks and supports among arguments. We study different semantic interpretations of support in BAFs, particularly necessary and deductive support, as well as argument coalitions and a recent proposal by Gabbay. We analyse the relationship of these different notions of support in BAFs with the semantics of a well established structured argumentation formalism, Assumption-Based Argumentation (ABA), which predates BAFs. We propose natural mappings from BAFs into a restricted class of (non-flat) ABA frameworks, which we call bipolar, and prove that the admissible and preferred semantics of these ABA frameworks correspond to the admissible and preferred semantics of the various approaches to BAFs. Motivated by the definition of stable semantics for BAFs, we introduce a novel set-stable semantics for ABA frameworks, and prove that it corresponds to the stable semantics of the various approaches to BAFs. Finally, as a by-product of modelling various approaches to BAFs in bipolar ABA, we identify precise semantic relationships amongst all approaches we consider.

  • Conference paper
    Cocarascu O, Toni F, 2017,

    Identifying attack and support argumentative relations using deep learning

    , 2017 Conference on Empirical Methods in Natural Language Processing, Publisher: Association for Computational Linguistics, Pages: 1374-1379

    We propose a deep learning architecture tocapture argumentative relations ofattackandsupportfrom one piece of text to an-other, of the kind that naturally occur ina debate. The architecture uses two (uni-directional or bidirectional) Long Short-Term Memory networks and (trained ornon-trained) word embeddings, and al-lows to considerably improve upon exist-ing techniques that use syntactic featuresand supervised classifiers for the sameform of (relation-based) argument mining.

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