A WoZ Study for an Incremental Proficiency Scoring Interview Agent Eliciting Ratable Samples

Mao Saeki*, Weronika Demkow, Tetsunori Kobayashi, Yoichi Matsuyama

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

To assess the conversational proficiency of language learners, it is essential to samples that are representative of the learner’s full linguistic ability. This is realized through the adjustment of oral interview questions to the learner’s perceived proficiency level. An automatic system eliciting ratable samples must incrementally predict the approximate proficiency from a few turns of dialog and employ an adaptable question generation strategy according to this prediction. This study investigates the feasibility of such incremental adjustment of oral interview question difficulty during the interaction between a virtual agent and learner. First, we create an interview scenario with questions designed for different levels of proficiency and collect interview data using a Wizard-of-Oz virtual agent. Next, we build an incremental scoring model and analyze the accuracy. Finally, we discuss the future direction of automated adaptive interview system design.

本文言語English
ホスト出版物のタイトルConversational AI for Natural Human-Centric Interaction - 12th International Workshop on Spoken Dialogue System Technology, IWSDS 2021
編集者Svetlana Stoyanchev, Stefan Ultes, Haizhou Li
出版社Springer Science and Business Media Deutschland GmbH
ページ193-201
ページ数9
ISBN(印刷版)9789811955372
DOI
出版ステータスPublished - 2022
イベント12th International Workshop on Spoken Dialogue System Technology, IWSDS 2021 - Virtual, Online
継続期間: 2021 11月 152021 11月 17

出版物シリーズ

名前Lecture Notes in Electrical Engineering
943
ISSN(印刷版)1876-1100
ISSN(電子版)1876-1119

Conference

Conference12th International Workshop on Spoken Dialogue System Technology, IWSDS 2021
CityVirtual, Online
Period21/11/1521/11/17

ASJC Scopus subject areas

  • 産業および生産工学

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