提升口语评估准确性，融合图文与细粒度语法分析
Advancing Automated Speaking Assessment Leveraging Multifaceted Relevance and Grammar Information
- 引入多维度相关性模块，融合问题、图像、范例和回答内容
- 通过语法纠错技术识别具体错误类型，提升语言使用评分精度
- 适合需要全面口语评估的教育场景或智能评测系统开发者
当前自动口语评估系统在多维度评价中常未能充分利用内容相关性，忽视图像或范例线索，且语法分析流于表面，缺乏错误类型细节。本文提出两项改进：一是构建多维度相关性模块，整合问题、关联图像、范例及二语学习者口语回答，实现内容相关性的综合评估；二是基于先进的语法纠错（GEC）与精细标注，提取细粒度语法错误特征，识别具体错误类别。实验与消融研究证实，这些组件显著提升了内容相关性、语言使用及整体评估性能，表明采用更丰富、更细致的特征集对实现全面口语评估具有显著优势。
原文摘要 · Abstract (English)
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.
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