This paper provides a new approach to the study of linguistic differences in research articles written by native and non-native English writers, including a novel linguistic resource. Conceptually, we propose a functional definition of academic nativeness. Empirically, we operationalize this definition through a survey and the development of a reliable automatic method to distinguish academic natives from non-natives. We then release a corpus of 80 research articles in the field of Linguistics, with introductions manually and reliably annotated for dialogical moves. Preliminary experiments indicate that automatic annotation using large language models remains challenging. Furthermore, the linguistic features that differentiate academic natives and non-natives diverge from those typically associated with general native/non-native English distinctions. These findings aim to inform and enhance academic writing pedagogy, while also offering insights relevant to broader language and corpus studies, as well as computational research.

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.

Within the financial communication domain, Earnings Conference Calls (ECCs) play a pivotal role in tracing (a) the presentational strategies and trust-building devices used by company representatives and (b) the relevant hot-topics for stakeholders, from which they form an (e)valuation of the company. Due to their formally regulated nature, ECCs are a favoured domain for the study of argumentation in context and the extraction of Argumentative Discourse Units (ADUs). However, the idiosyncratic structure of dialogical exchanges in Q&A sessions of ECCs, particularly at the level of question formulation, challenges existing models of argument mining, which assume adjacency of related question and answer turns in the dialogue. Maximal Interrogative Units (MIUs) are a novel approach to grouping together topically contiguous argumentative components within a question turn. MIU identification allows application of existing argument mining techniques to a less noisy unit of text, following removal of discourse regulators and splitting into sub-units of thematically related text. Evaluation of an automated method for MIU recognition is also presented with respect to gold-standard manual annotation.