Assessment of Nonverbal-behavior Annotation Tags in Multimodal Learner Corpus

Abstract

Learner corpus research has revealed the appropriateness of language learners’ language use by analyzing corpus data including annotation tags. Annotation tags provide linguistic information such as part-of-speech and error information on lexical, syntactic, semantic, and phonetic items. Recent learner corpus research compiling a multimodal learner corpus has extended the research target to nonverbal behaviors such as facial expressions and gesturing because nonverbal behaviors play a significant role in communication. The goal of this paper is two-fold. The first objective is to validate nonverbal-behavior annotation tags of the previous multimodal learner corpora. The second objective is to propose a plausible nonverbal-behavior tag set for a multimodal learner corpus.