Span Identification of Epistemic Stance-Taking in Academic Written English
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Abstract (arXiv)
Responding to the increasing need for automated writing evaluation (AWE) systems to assess language use beyond lexis and grammar (Burstein et al., 2016), we introduce a new approach to identify rhetorical features of stance in academic English writing. Drawing on the discourse-analytic framework of engagement in the Appraisal analysis (Martin & White, 2005), we manually annotated 4,688 sentences (126,411 tokens) for eight rhetorical stance categories (e.g., PROCLAIM, ATTRIBUTION) and additional discourse elements. We then report an experiment to train machine learning models to identify and categorize the spans of these stance expressions. The best-performing model (RoBERTa + LSTM) achieved macro-averaged F1 of .7208 in the span identification of stance-taking expressions, slightly outperforming the intercoder reliability estimates before adjudication (F1 = .6629).
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 61 references that are in the map. First posted on arXiv 3 Jun 2023.
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10 of 61 references.
References · 10
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- 2022An Empirical Study on Finding Spanscited by 2
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