This study aims to model the discourse structure of spontaneous spoken responses within the context of an assessment of English speaking proficiency for non-native speakers. Rhetorical Structure Theory (RST) has been commonly used in the analysis of discourse organization of written texts; however, limited research has been conducted to date on RST annotation and parsing of spoken language, in particular, non-native spontaneous speech. Due to the fact that the measurement of discourse coherence is typically a key metric in human scoring rubrics for assessments of spoken language, we conducted research to obtain RST annotations on non-native spoken responses from a standardized assessment of academic English proficiency. Subsequently, automatic parsers were trained on these annotations to process non-native spontaneous speech. Finally, a set of features were extracted from automatically generated RST trees to evaluate the discourse structure of non-native spontaneous speech, which were then employed to further improve the validity of an automated speech scoring system.

This study aims to build an automatic system for the detection of plagiarized spoken responses in the context of an assessment of English speaking proficiency for non-native speakers. Classification models were trained to distinguish between plagiarized and non-plagiarized responses with two different types of features: text-to-text content similarity measures, which are commonly used in the task of plagiarism detection for written documents, and speaking proficiency measures, which were specifically designed for spontaneous speech and extracted using an automated speech scoring system. The experiments were first conducted on a large data set drawn from an operational English proficiency assessment across multiple years, and the best classifier on this heavily imbalanced data set resulted in an F1-score of 0.761 on the plagiarized class. This system was then validated on operational responses collected from a single administration of the assessment and achieved a recall of 0.897. The results indicate that the proposed system can potentially be used to improve the validity of both human and automated assessment of non-native spoken English.