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.

翻译：我们通过两个分别聚焦于复杂句子与复杂词汇识别的语料库，对生产系统中的西班牙语文本简化（TS）进行了评估。我们将最常用的西班牙语特定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文本简化的偏好方面始终表现更优。通过分析，我们发现多语言模型在同一任务上的表现不及等价的纯西班牙语模型，然而所有模型都过于频繁地关注虚假统计特征（如句子长度）。我们将评估中使用的语料库公开发布给更广泛的学术社区，以期推动西班牙语自然语言处理领域的前沿发展。