Advancing Automated Speaking Assessment Leveraging Multifaceted Relevance and Grammar Information
This work addresses limitations in automated speaking assessment for L2 speakers by enhancing content relevance and grammar analysis, representing an incremental advancement in the field.
The paper tackled the problem of automated speaking assessment (ASA) systems inadequately using content relevance and grammar analysis by introducing a multifaceted relevance module and fine-grained grammar error features, resulting in significant improvements in evaluating content relevance, language use, and overall ASA performance.
Current automated speaking assessment (ASA) systems for use in multi-aspect evaluations often fail to make full use of content relevance, overlooking image or exemplar cues, and employ superficial grammar analysis that lacks detailed error types. This paper ameliorates these deficiencies by introducing two novel enhancements to construct a hybrid scoring model. First, a multifaceted relevance module integrates question and the associated image content, exemplar, and spoken response of an L2 speaker for a comprehensive assessment of content relevance. Second, fine-grained grammar error features are derived using advanced grammar error correction (GEC) and detailed annotation to identify specific error categories. Experiments and ablation studies demonstrate that these components significantly improve the evaluation of content relevance, language use, and overall ASA performance, highlighting the benefits of using richer, more nuanced feature sets for holistic speaking assessment.