ReMLAV: Relational Machine Learning for Argument Validation

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We focus on argument validation, one important aspect of argumentation machines, and formalize the problem as the validation of what we call an argument link: two consecutive sentences in an argument. An argument link is classified as valid if and only if the two sentences form a valid part of an argument, e.g., because the first entails or logically implies the second. We propose a new argument link validation method that is based on three bodies of prior work: (i) relational machine learning, (ii) embeddings and(iii) subspace analysis. (i) Relational machine learning is concerned with models of relations between objects; we apply it here by modeling sentences as objects and relations as (in)validity or (on a finer grain) as entailment, causality, contradiction etc. (ii) Embeddings are high-dimensional representations of linguistic objects and support powerful models that generalize well to new data. We apply them here to representing sentences in arguments. (iii) Embeddings have the drawback that they are difficult to inspect andunderstand. But this ability is critical for argumentation machines that are usable by humans. We develop subspace models of the embedding space that support humans in understanding sentence embedding spaces and argumentation machine decisions.