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• 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.

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• 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.

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• 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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