  In this paper, we present and implement a multi-dimensional, modular
framework for performing deep argument analysis (DeepA2) using current
pre-trained language models (PTLMs). ArgumentAnalyst -- a T5 model (Raffel et
al. 2020) set up and trained within DeepA2 -- reconstructs argumentative texts,
which advance an informal argumentation, as valid arguments: It inserts, e.g.,
missing premises and conclusions, formalizes inferences, and coherently links
the logical reconstruction to the source text. We create a synthetic corpus for
deep argument analysis, and evaluate ArgumentAnalyst on this new dataset as
well as on existing data, specifically EntailmentBank (Dalvi et al. 2021). Our
empirical findings vindicate the overall framework and highlight the advantages
of a modular design, in particular its ability to emulate established
heuristics (such as hermeneutic cycles), to explore the model's uncertainty, to
cope with the plurality of correct solutions (underdetermination), and to
exploit higher-order evidence.
