language:
- en
license: cc-by-sa-4.0
datasets:
- debatelab/aaac
widget:
- text: >-
reason_statements: argument_source: If Peter likes fish, Peter has been to
New York. So, Peter has been to New York.
example_title: Premise identification
- text: >-
argdown_reconstruction: argument_source: If Peter likes fish, Peter has
been to New York. So, Peter has been to New York.
example_title: Argdown reconstruction
- text: >-
premises_formalized: reason_statements: If Peter likes fish, Peter has
been to New York. (ref: (1))
example_title: Formalization
inference:
parameters:
max_length: 80
Pretraining Dataset: AAAC01
Demo: DeepA2 Demo
Authors: Gregor Betz, Kyle Richardson
Abstract
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.