Xinhao Wang, Binod Gyawali, James V. Bruno, Hillary R. Molloy, Keelan Evanini, K. Zechner
{"title":"Discourse Modeling of Non-Native Spontaneous Speech Using the Rhetorical Structure Theory Framework","authors":"Xinhao Wang, Binod Gyawali, James V. Bruno, Hillary R. Molloy, Keelan Evanini, K. Zechner","doi":"10.1109/SLT.2018.8639509","DOIUrl":null,"url":null,"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.","PeriodicalId":377307,"journal":{"name":"2018 IEEE Spoken Language Technology Workshop (SLT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Spoken Language Technology Workshop (SLT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SLT.2018.8639509","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.