Span Identification of Epistemic Stance-Taking in Academic Written
  English
Masaki Eguchi Kristopher Kyle
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).