@inproceedings{zaitova-etal-2022-mapping,
    title = "Mapping Phonology to Semantics: A Computational Model of Cross-Lingual Spoken-Word Recognition",
    author = "Zaitova, Iuliia  and
      Abdullah, Badr  and
      Klakow, Dietrich",
    editor = {Scherrer, Yves  and
      Jauhiainen, Tommi  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Nakov, Preslav  and
      Tiedemann, J{\"o}rg  and
      Zampieri, Marcos},
    booktitle = "Proceedings of the Ninth Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.vardial-1.6/",
    pages = "54--63",
    abstract = "Closely related languages are often mutually intelligible to various degrees. Therefore, speakers of closely related languages are usually capable of (partially) comprehending each other{'}s speech without explicitly learning the target, second language. The cross-linguistic intelligibility among closely related languages is mainly driven by linguistic factors such as lexical similarities. This paper presents a computational model of spoken-word recognition and investigates its ability to recognize word forms from different languages than its native, training language. Our model is based on a recurrent neural network that learns to map a word{'}s phonological sequence onto a semantic representation of the word. Furthermore, we present a case study on the related Slavic languages and demonstrate that the cross-lingual performance of our model not only predicts mutual intelligibility to a large extent but also reflects the genetic classification of the languages in our study."
}