A Computational Model for the Assessment of Mutual Intelligibility Among Closely Related Languages
This work addresses the challenge of automatically testing mutual intelligibility for linguists and computational researchers, but it is incremental as it extends an existing method to multilingual settings.
The paper tackled the problem of assessing mutual intelligibility among closely related languages by proposing a computational model using Linear Discriminative Learning with multilingual semantic vectors and sound classes, tested on German, Dutch, and English, finding that comprehension accuracy depends on inflection trimming and language pair.
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.