The traditional approach to morphological inflection (the task of modifying a base word (lemma) to express grammatical categories) has been, for decades, to consider lexical entries of lemma-tag-form triples uniformly, lacking any information about their frequency distribution. However, in production deployment, one might expect the user inputs to reflect a real-world distribution of frequencies in natural texts. With future deployment in mind, we explore the incorporation of corpus frequency information into the task of morphological inflection along three key dimensions during system development: (i) for train-dev-test split, we combine a lemma-disjoint approach, which evaluates the model's generalization capabilities, with a frequency-weighted strategy to better reflect the realistic distribution of items across different frequency bands in training and test sets; (ii) for evaluation, we complement the standard type accuracy (often referred to simply as accuracy), which treats all items equally regardless of frequency, with token accuracy, which assigns greater weight to frequent words and better approximates performance on running text; (iii) for training data sampling, we introduce a method novel in the context of inflection, frequency-aware training, which explicitly incorporates word frequency into the sampling process. We show that frequency-aware training outperforms uniform sampling in 26 out of 43 languages.

翻译：传统上，形态屈折变化（即修改基词（词元）以表达语法范畴的任务）的方法数十年来一直是将词元-标签-形式三元组的词条视为均匀的，缺乏其频率分布的任何信息。然而，在实际生产部署中，人们可能期望用户输入能反映自然文本中频率的真实世界分布。着眼于未来的部署，我们在系统开发的三个关键维度上探索了将语料库频率信息纳入形态屈折变化任务的方法：(i) 在训练-开发-测试集划分方面，我们结合了词元不相交的方法（用于评估模型的泛化能力）与频率加权策略，以更好地反映训练集和测试集中不同频段项目的真实分布；(ii) 在评估方面，我们补充了标准的类型准确率（通常简称为准确率，即平等对待所有项目而不考虑频率），引入了词例准确率，该指标赋予高频词更大权重，能更好地近似模型在连续文本上的性能；(iii) 在训练数据采样方面，我们引入了一种在屈折变化背景下新颖的方法——频率感知训练，该方法在采样过程中明确纳入了词频。我们证明，在43种语言中的26种里，频率感知训练的表现优于均匀采样。