Abstract
Identifying cultural capital (CC) themes in student reflections can offer valuable insights that help foster equitable learning environments in classrooms. However, themes such as aspirational goals or family support are often woven into narratives, rather than appearing as direct keywords. This makes them difficult to detect for standard NLP models that process sentences in isolation. The core challenge stems from a lack of awareness, as standard models are pre-trained on general corpora, leaving them blind to the domain-specific language and narrative context inherent to the data. To address this, we introduce AWARE, a framework that systematically attempts to improve a transformer model's awareness for this nuanced task. AWARE has three core components: 1) Domain Awareness, adapting the model's vocabulary to the linguistic style of student reflections; 2) Context Awareness, generating sentence embeddings that are aware of the full essay context; and 3) Class Overlap Awareness, employing a multi-label strategy to recognize the coexistence of themes in a single sentence. Our results show that by making the model explicitly aware of the properties of the input, AWARE outperforms a strong baseline by 2.1 percentage points in Macro-F1 and shows considerable improvements across all themes. This work provides a robust and generalizable methodology for any text classification task in which meaning depends on the context of the narrative.
Abstract (translated)
在学生反思中识别文化资本（CC）主题可以为促进课堂中的公平学习环境提供有价值的见解。然而，诸如抱负目标或家庭支持等主题通常交织于叙述之中，并未以直接关键词的形式出现，这使得标准自然语言处理（NLP）模型难以检测这些主题，因为它们在处理句子时孤立地工作。这一核心挑战源于现有模型缺乏领域意识：标准模型是基于通用语料库预训练的，因而无法识别数据中特有的领域特定语言和叙述上下文。 为了解决这个问题，我们提出了AWARE框架，该框架系统性地尝试提高转换器模型在这项细致任务中的认知能力。AWARE包括三个核心组成部分： 1. **领域意识**：调整模型词汇以适应学生反思的语体风格。 2. **情境意识**：生成能够反映整篇文章上下文的句子嵌入。 3. **类别重叠意识**：采用多标签策略，识别单个句子中主题共存的情况。 我们的实验结果表明，通过使模型明确地了解输入的特点，AWARE在宏平均F1分数上超越了强大的基线模型2.1个百分点，并且在所有主题上都取得了显著的改进。这项工作为任何文本分类任务提供了一种稳健且通用的方法论，其中意义取决于叙述上下文。
URL
https://arxiv.org/abs/2510.04983
