[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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