论文标题
使用扩展的语音对齐方式将反射预测作为分类问题作为分类问题
Approaching Reflex Predictions as a Classification Problem Using Extended Phonological Alignments
论文作者
论文摘要
这项工作描述了对认知反射预测的“扩展对齐”（或“多层”）方法的实现，并提交给“同源反射的预测”共享任务。与List2022D相似，该技术涉及与多层矢量进行序列对齐的自动扩展，这些矢量对两个站点特异性性状（例如声音类别和独特特征）以及上下文和上下文段的序列编码信息层，并由交叉站点推荐和复制传达。该方法允许将同源反射预测的问题概括为分类问题，并使用平行的同源集训练模型。使用随机森林的模型对反射预测的共享任务进行了训练和评估，并对实验结果进行了介绍和讨论，并与其他实现进行了一些差异。
This work describes an implementation of the "extended alignment" (or "multitiers") approach for cognate reflex prediction, submitted to "Prediction of Cognate Reflexes" shared task. Similarly to List2022d, the technique involves an automatic extension of sequence alignments with multilayered vectors that encode informational tiers on both site-specific traits, such as sound classes and distinctive features, as well as contextual and suprasegmental ones, conveyed by cross-site referrals and replication. The method allows to generalize the problem of cognate reflex prediction as a classification problem, with models trained using a parallel corpus of cognate sets. A model using random forests is trained and evaluated on the shared task for reflex prediction, and the experimental results are presented and discussed along with some differences to other implementations.
