We present a corpus of Multilingual Argumentative Deliberation (MAD), a manually annotated corpus of deliberative dialogues in English, German, Polish and Italian. Four groups each completed two variants of a ranking task, the NASA Survival Scenario; once in their native language and once in English. The corpus is annotated using Inference Anchoring Theory (IAT), a framework developed for analysing argument in dialogical settings, and widely used in argument mining. As an argument mining resource, MAD is distinct in offering equivalent instances of spontaneous argumentation across languages. In addition to use in argument mining, the annotation captures both argument relations and dialogue acts, enabling deeper analysis of argument and dialogue structure than typical of argument-only corpora. The design of the corpus enables studies of second-language effects in English-medium interaction, cross-linguistic argument comparisons for German, Polish and Italian, and speaker dialogue strategy consistency, amongst others. The primary annotated MAD corpus is freely available at https://corpora.aifdb.org/mad, while we additionally release the unannotated transcripts to facilitate repurposing of the material.

Despite extensive research in Argument Mining (AM), the field faces significant challenges in limited reproducibility, difficulty in comparing systems due to varying task combinations, and a lack of interoperability caused by the heterogeneous nature of argumentation theory. These challenges are further exacerbated by the absence of dedicated tools, with most advancements remaining isolated research outputs rather than reusable systems. The oAMF (Open Argument Mining Framework) addresses these issues by providing an open-source, modular, and scalable platform that unifies diverse AM methods. Initially released with seventeen integrated modules, the oAMF serves as a starting point for researchers and developers to build, experiment with, and deploy AM pipelines while ensuring interoperability and allowing multiple theories of argumentation to co-exist within the same framework. Its flexible design supports integration via Python APIs, drag-and-drop tools, and web interfaces, streamlining AM development for research and industry setup, facilitating method comparison, and reproducibility.

Certain conditionals have something other than a clause as their consequent: their antecedent if-clauses are ‘adverbial clauses’ without a verb. We argue that they function in a way already seen for those with clausal consequents, despite lacking the content we might expect for the formation of a conditional. The use of the if-clause with sub-clausal consequents is feasible thanks to the fact that this function does not depend on the consequent content, and so is not impeded when the consequent does not provide a proposition, question or imperative. To support this we provide meaning rules for conditionals in terms of information state updates, letting the same construction play out in different ways depending on context and content.

To model conditionals in a way that reflects their acceptability, we must include some means of making judgements about whether antecedent and consequent are meaningfully related or not. Enthymemes are non-logical arguments which do not hold up by themselves, but are acceptable through their relation to a topos, an already-known general principle or pattern for reasoning. This paper uses enthymemes and topoi as a way to model the world-knowledge behind these judgements. In doing so, it provides a reformalisation (in TTR) of enthymemes and topoi as networks rather than functions, and information state update rules for conditionals.