Extracting a Prototypical Argumentative Pattern in Financial Q&As

Argumentative patterns are recurrent strategies adopted to pursue a definite communicative goal in a discussion. For instance, in Q&A exchanges during financial conference calls, a pattern called Request of Confirmation of Inference (ROCOI) helps streamline conversations by requesting explicit verification of inferences drawn from a statement. Our work presents two ROCOI extraction approaches from interrogative units: sequence labeling and text-to-text generation. We experiment with multiple models for each task formulation to explore which models can effectively and robustly perform pattern extraction. Results indicate that machine-based ROCOI extraction is an achievable task, though variation among metrics that are designed for different evaluation dimensions makes obtaining a clear picture difficult. We find that overall, ROCOI extraction is performed best via sequence labeling, though with ample room for improvement. We encourage future work to extend the study to new argumentative patterns.

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Publication Details

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Discovery Research Portal (University of Dundee)
Published
2025-09-01
Primary Topic
Topic Modeling
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article
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article

Extracting a Prototypical Argumentative Pattern in Financial Q&As

Giulia D'Agostino, Chris; id_orcid 0000-0002-6849-1374 Reed, Michiel van der Meer
Discovery Research Portal (University of Dundee)
Topic Modeling
article

Extracting a Prototypical Argumentative Pattern in Financial Q&As

Giulia D'Agostino, Chris; id_orcid 0000-0002-6849-1374 Reed, Michiel van der Meer
article en

Abstract

Argumentative patterns are recurrent strategies adopted to pursue a definite communicative goal in a discussion. For instance, in Q&A exchanges during financial conference calls, a pattern called Request of Confirmation of Inference (ROCOI) helps streamline conversations by requesting explicit verification of inferences drawn from a statement. Our work presents two ROCOI extraction approaches from interrogative units: sequence labeling and text-to-text generation. We experiment with multiple models for each task formulation to explore which models can effectively and robustly perform pattern extraction. Results indicate that machine-based ROCOI extraction is an achievable task, though variation among metrics that are designed for different evaluation dimensions makes obtaining a clear picture difficult. We find that overall, ROCOI extraction is performed best via sequence labeling, though with ample room for improvement. We encourage future work to extend the study to new argumentative patterns.

Discovery Research Portal (University of Dundee)
Openalex Percentile: Top 15%
Topic Modeling
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