A Computational Model for the Assessment of Mutual Intelligibility Among
  Closely Related Languages
Jessica Nieder Johann-Mattis List
Closely related languages show linguistic similarities that allow speakers of
one language to understand speakers of another language without having actively
learned it. Mutual intelligibility varies in degree and is typically tested in
psycholinguistic experiments. To study mutual intelligibility computationally,
we propose a computer-assisted method using the Linear Discriminative Learner,
a computational model developed to approximate the cognitive processes by which
humans learn languages, which we expand with multilingual semantic vectors and
multilingual sound classes. We test the model on cognate data from German,
Dutch, and English, three closely related Germanic languages. We find that our
model's comprehension accuracy depends on 1) the automatic trimming of
inflections and 2) the language pair for which comprehension is tested. Our
multilingual modelling approach does not only offer new methodological findings
for automatic testing of mutual intelligibility across languages but also
extends the use of Linear Discriminative Learning to multilingual settings.