A Computational Model for the Assessment of Mutual Intelligibility Among Closely Related Languages (2024)

Abstract: 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.

## introduction speakers of a given language can often partially comprehend other languages in the same language family. this mutual intelligibility has been demonstrated to be dependent on several linguistic variables, such as phonological, orthographic or lexical similarity, and extralinguistic factors, such as the amount of previous exposure to or the attitude towards the other language @xcite @xcite . in addition, the phonetic similarity between words expressing similar meanings has been shown to be a major factor driving cross-linguistic mutual intelligibility @xcite . phonetically and semantically similar words are often called cognates in studies on mutual intelligibility, foreign language learning, and bilingualism @xcite . originally, however, the term denotes words inherited from the same ancestral language in genetically related languages @xcite . although cognates in the original sense often exhibit phonetic and semantic similarity across related languages, they do not necessarily do so, and words can also be similar in pronunciation and meaning due to other factors, including -most importantlyintensive borrowing, and -to a much lower degree -different kinds of sound symbolism (see @xcite for more details on the difference between mutual intelligibility and genetic relationship). due to a focus on the abilities of language users, research on mutual intelligibility often involves experimental studies with different groups and numbers of participants. experiments are diverse, usually consisting of certain comprehension tasks. experimental studies show some general limitations, in so far as uniform methods are rarely used (1), finding participants with a minimum or no exposure to the test language is difficult (2), and comparing several languages simultaneously is a time-and resource-consuming effort (3) @xcite . @xcite present a large-scale study on mutual intelligibility of five germanic languages using a cloze test, i.e. a written or audibly presented text in the target language with gaps that need to be filled in. however, they report a substantial loss in the number of participants when testing inherent intelligibility, the ability to comprehend the target language with no or little previous exposure @xcite . in an ideal setting with zero exposure to the target language, inherent intelligibility captures how comprehensible the target language is based on structural similarities only. this, in turn, would offer insights into what linguistic structures give rise to mutual intelligibility without extralinguistic or other language exposure-based interference. in reality, the goal of finding participants with no or a minimum of exposure to certain languages is an almost impossible requirement to fulfill due to the status of some languages of being a common lingua franca @xcite . in this study we propose a computer-assisted method couched in the discriminative lexicon framework by @xcite to assess mutual intelligibility in germanic languages. by focusing on computational methods instead of human subjects we can overcome the mentioned limitations. our proposed model does not involve the recruitment of participants, there are no extralinguistic factors nor target language exposure involved in training. we offer a uniform method that can be adapted to various language families and lead to new insights into intelligibility based on a careful selection of linguistic factors that are involved in language comprehension. ## linear discriminative learning with the discriminative lexicon framework (dl), @xcite propose a model of language processing that explores the cognitive mapping mechanisms involved in language learning. language comprehension is understood as a mapping of phonological forms onto meaning @xcite . mathematically, it is implemented as multivariate multiple regression in the linear discriminative learner (ldl) model. given a phonological matrix c and a semantic matrix s, the comprehension matrix f is obtained by post-multiplying c with f : @xmath0. the f matrix then specifies the associaton weights between all phonological cues and all semantic dimensions @xcite . multiplying c with f finally predicts the semantic vector ŝ for all input word forms that can be used for evaluating comprehension accuracy of the model. computationally, the ldl model conceptualizes language comprehension as a simple artificial neural network directly connecting phonological and semantic vectors without any hidden layers @xcite . in this study, we make use of ldl to explore the mutual comprehension of the germanic languages dutch, german and english based on a cross-language learning setting (see also @xcite , for another multilingual approach using ldl). as phonological input we use cognate sets from all languages. for the semantic matrix, we opted for the multilingual conceptnet number-batch word embeddings version 19.08 from @xcite that offer the possibility to directly compare the meaning of cognate concepts. ## discussion and conclusion in this study we presented a computer-assisted method to mutual intelligibility based on a model that captures the cognitive processes by which humans comprehend languages. we expanded the model with multilingual semantic vectors and multilingual sound classes. our multilingual sound classes were predictive when a combination of at least 3 sound classes is given, indicating that knowing the order of sound classes allows the model to comprehend languages from the same language family. however, we observe an effect of the training language, with english being the least advantageous language in our setting and in the data of @xcite with human participants. we report a higher accuracy for german-english than german-dutch, again in line with the human data of @xcite . if sound classes are trimmed, we find the opposite effect. the pair dutch-english shows better comprehension accuracies than dutch-german, again with the opposite picture for the trimmed version. from a language learning perspective, the change of direction, i.e. the better prediction for german-dutch and dutch-german after trimming would imply a certain morphological knowledge of speakers. speakers of german or dutch knowing verb endings and ignoring them purposefully have an advantage in comprehending english. our proposed model does not only offer a new method for automatic testing of mutual intelligibility but shows clear similarities to data obtained from human participants, making it a useful cognitive tool for research on language comprehension.