以用户为中心的西班牙语文本简化评估
A User-Centered Evaluation of Spanish Text Simplification
AI总结：
该研究使用复杂句与复杂词语料评估生产环境中的西班牙语文本简化，发现神经网络优于可读性评分、单语模型优于多语言模型，并指出现有模型依赖虚假特征。
摘要：
我们针对一个生产系统中的西班牙语文本简化（TS）提出了一项评估，使用了两个分别聚焦复杂句和复杂词识别的语料库。我们将最主流的西班牙语专用可读性评分与神经网络进行比较，结果表明后者在预测用户对TS的偏好方面始终表现更好。作为分析的一部分，我们发现多语言模型在同一任务上的表现不及等效的仅西班牙语模型，但所有模型都过于频繁地关注句子长度等虚假统计特征。我们向更广泛的社区发布评估中使用的语料库，希望推动西班牙语自然语言处理的最先进水平。
英文摘要：
We present an evaluation of text simplification (TS) in Spanish for a production system, by means of two corpora focused in both complex-sentence and complex-word identification. We compare the most prevalent Spanish-specific readability scores with neural networks, and show that the latter are consistently better at predicting user preferences regarding TS. As part of our analysis, we find that multilingual models underperform against equivalent Spanish-only models on the same task, yet all models focus too often on spurious statistical features, such as sentence length. We release the corpora in our evaluation to the broader community with the hopes of pushing forward the state-of-the-art in Spanish natural language processing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
