[论文] A Durability and Cross-Language Transfer Benchmark for a Validate...

论文概要

研究领域: NLP
作者: Esteban U. Vega Barajas
发布时间: 2026-07-13
arXiv: 2607.11873

中文摘要

机构收集的开放式教学评估反馈远超其阅读能力。先前研究提出了一种经验证的教学反馈分类协议，基于文档化标注指南、标注者内信度测量、分层交叉验证和冻结编码器设计在西班牙语机构语料上评估。其复用受限于两个问题：固定于2019年冻结嵌入的协议在表示方法进步后是否仍具竞争力，以及是否能迁移到第二语言。本文在原始西班牙语数据上跨三代表示方法（稀疏词汇特征、冻结Transformer嵌入、提示大语言模型）重新运行该协议，并将其情感任务迁移到英语的45000条平衡评论语料。结果显示该协议具有持久性：2026前沿模型在最难的西班牙语主题任务上取得最高F1，但在情感任务上并无优势，且与廉价模型在英语上无描述性分离，因此模型选择是部署决策而非方法属性。

原文摘要

Institutions collect far more open-ended teaching-evaluation feedback than they read. A prior study introduced a validated protocol for classifying such comments by thematic category and sentiment, built from a documented annotation guide, an intra-annotator reliability measurement, stratified cross-validation, and a held-out evaluation on a Spanish institutional corpus with a frozen-encoder design. Two questions limit its reuse: whether a protocol fixed to 2019-era frozen embeddings stays competitive as representation methods advance, and whether it transfers to a second language. We re-run it on the original Spanish data across three representation generations, sparse lexical features, frozen transformer embeddings, and prompted large language models, and transfer its sentiment task to E...

自动采集于 2026-07-15

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