Abstract
Discourse modes play an important role in writing composition and evaluation. This paper presents a study on the manual and automatic identification of narration,exposition, description, argument and emotion expressing sentences in narrative essays. We annotate a corpus to study the characteristics of discourse modes and describe a neural sequence labeling model for identification. Evaluation results show that discourse modes can be identified automatically with an average F1-score of 0.7. We further demonstrate that discourse modes can be used as features that improve automatic essay scoring (AES). The impacts of discourse modes for AES are also discussed.
Highlights
Discourse modes, known as rhetorical modes, describe the purpose and conventions of the main kinds of language based communication.Most common discourse modes include narration, description, exposition and argument
We demonstrate the effectiveness of taking discourse modes into account for automatic essay scoring
We can see that the Bayesian linear ridge regression (BLRR) algorithm performs better than the support vector regression (SVR) algorithm
Summary
Known as rhetorical modes, describe the purpose and conventions of the main kinds of language based communication.Most common discourse modes include narration, description, exposition and argument. A typical text would make use of all the modes, in a given one there will often be a main mode. Despite their importance in writing composition and assessment (Braddock et al, 1963), there is relatively little work on analyzing discourse modes based on computational models. The use of discourse modes is important in writing composition, because they relate to several aspects that would influence the quality of a text. Discourse modes reflect the organization of a text. Meurer (2002) points that discourse modes stand for unity as they constitute general patterns of language organization strategically used by the writer. The combination and interaction of various discourse modes make an organized unified text
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