Jessica Nieder
2024
A Computational Model for the Assessment of Mutual Intelligibility Among Closely Related Languages
Jessica Nieder
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Johann-Mattis List
Proceedings of the 6th Workshop on Research in Computational Linguistic Typology and Multilingual NLP
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.
Generating Feature Vectors from Phonetic Transcriptions in Cross-Linguistic Data Formats
Arne Rubehn
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Jessica Nieder
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Robert Forkel
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Johann-Mattis List
Proceedings of the Society for Computation in Linguistics 2024
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