论文标题
神经无监督的原始语言形式的重建
Neural Unsupervised Reconstruction of Protolanguage Word Forms
论文作者
论文摘要
我们提出了一种最先进的神经方法，用于对古代单词形式的无监督重建。该领域中的先前工作使用了期望最大化，以预测古代形式与其认知之间的简单语音变化。我们通过神经模型扩展了这项工作，可以捕获更复杂的语音和形态变化。同时，我们通过在模型中构建单调对准约束并在最大化步骤中故意不适用，从而保留经典方法的归纳偏见。我们评估了从五种浪漫语言中认知数据集重建拉丁语的任务的性能，与以前的方法相比，与目标单词形式的编辑距离显着降低。
We present a state-of-the-art neural approach to the unsupervised reconstruction of ancient word forms. Previous work in this domain used expectation-maximization to predict simple phonological changes between ancient word forms and their cognates in modern languages. We extend this work with neural models that can capture more complicated phonological and morphological changes. At the same time, we preserve the inductive biases from classical methods by building monotonic alignment constraints into the model and deliberately underfitting during the maximization step. We evaluate our performance on the task of reconstructing Latin from a dataset of cognates across five Romance languages, achieving a notable reduction in edit distance from the target word forms compared to previous methods.
