2020/05/27 by Chundra Cathcart, Cathcart, Chundra A., Florian Wandl +1
Computer Science · Psychology · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Phonetics and Phonology Research #Speech Recognition and Synthesis
paper · pdf · doi:10.48550/arxiv.2005.13575
openalex publication_date 2020/05/27 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
This paper investigates the ability of neural network architectures to\neffectively learn diachronic phonological generalizations in a multilingual\nsetting. We employ models using three different types of language embedding\n(dense, sigmoid, and straight-through). We find that the Straight-Through model\noutperforms the other two in terms of accuracy, but the Sigmoid model's\nlanguage embeddings show the strongest agreement with the traditional\nsubgrouping of the Slavic languages. We find that the Straight-Through model\nhas learned coherent, semi-interpretable information about sound change, and\noutline directions for future research.\n