Is Argument Structure of Learner Chinese Understandable: A Corpus-Based Analysis

TL;DR

This study analyzes argument structure errors in learner Chinese using a corpus with semantic role labeling, showing high understandability of learner texts and identifying common competence errors.

Contribution

It introduces a corpus-based approach with semantic role annotations to analyze argument structure errors in learner Chinese, demonstrating the applicability of the Chinese PropBank for second language data.

Findings

High inter-annotator agreement indicates learner texts are understandable to native speakers.

Identifies common errors such as word order, word choice, and argument confounding.

Semantic role labeling is effective for analyzing learner language phenomena.

Abstract

This paper presents a corpus-based analysis of argument structure errors in learner Chinese. The data for analysis includes sentences produced by language learners as well as their corrections by native speakers. We couple the data with semantic role labeling annotations that are manually created by two senior students whose majors are both Applied Linguistics. The annotation procedure is guided by the Chinese PropBank specification, which is originally developed to cover first language phenomena. Nevertheless, we find that it is quite comprehensive for handling second language phenomena. The inter-annotator agreement is rather high, suggesting the understandability of learner texts to native speakers. Based on our annotations, we present a preliminary analysis of competence errors related to argument structure. In particular, speech errors related to word order, word selection, lack of proposition, and argument-adjunct confounding are discussed.