Discourse Modeling of Non-Native Spontaneous Speech Using the Rhetorical Structure Theory Framework

Xinhao Wang, Binod Gyawali, James V. Bruno, Hillary R. Molloy, Keelan Evanini, K. Zechner
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Abstract

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 initiated a research to first obtain RST annotations on non-native spoken responses from a standardized assessment of academic English proficiency. Afterwards, based on the annotations obtained, automatic parsers were built to process non-native spontaneous speech. Finally, a set of effective features were extracted from both manually annotated and automatically generated RST trees to evaluate the discourse structure of non-native spontaneous speech, and then employed to further improve the validity of an automated speech scoring system.
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基于修辞结构理论框架的非母语自发语篇建模
本研究的目的是在非英语母语者英语口语水平评估的背景下,建立自发口语反应的话语结构模型。修辞结构理论(RST)是分析书面语篇组织的常用理论。然而,到目前为止,对口语,特别是非母语自发语音的RST注释和解析的研究还很有限。由于语篇连贯性的测量通常是人类口语评估评分标准中的一个关键指标,因此我们发起了一项研究,首先从学术英语水平的标准化评估中获得非母语口语反应的RST注释。然后,基于获得的注释,构建自动解析器来处理非本地自发语音。最后,从人工标注和自动生成的RST树中提取一组有效的特征来评估非母语自发语音的话语结构,并进一步提高语音自动评分系统的有效性。
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