The paper presents the results of a free translation experiment, which was set up to explore Slavic cross-language intelligibility. In the experiment, native speakers of Russian were asked to read a sentence in one of the five Slavic languages and return a Russian translation of a highlighted item. The experiment is focused on microsyntactic units because they offer an increased intercomprehension difficulty due to opaque semantics. Each language is represented by at least 50 stimuli, and each stimulus has generated at least 20 responses. The levels of intercomprehension are captured by categorising participants’ responses into seven types of translation solutions (paraphrase, correct, fluent_literal, awkward_literal, fantasy, noise, and empty), generally reflecting the level of the cross-linguistic intelligibility of the stimuli. The study aims to reveal linguistic factors that favour intercomprehension across Slavic languages. We use regression and correlation analysis to identify the most important intercomprehension predictors and statistical analysis to bring up the most typical cases and outliers. We explore several feature types that reflect the properties of the translation tasks and their outcomes, including point-wise phonological and orthographic distances, cosine similarities, surprisals, translation quality scores and translation solution entropy indices. The experimental data confirms the expected gradual increase of intelligibility from West-Slavic to East-Slavic languages for the speakers of Russian. We show that intelligibility is highly contingent on the ability of speakers to recognise and interpret formal similarities between languages as well as on the size of these similarities. For several Slavic languages, the context sentence complexity was a significant predictor of intelligibility.

This study focuses on evaluating and predicting the intelligibility of non-compositional expressions within the context of five closely related Slavic languages: Belarusian, Bulgarian, Czech, Polish, and Ukrainian, as perceived by native speakers of Russian. Our investigation employs a web-based experiment where native Russian respondents take part in free-response and multiple-choice translation tasks. Based on the previous studies in mutual intelligibility and non-compositionality, we propose two predictive factors for reading comprehension of unknown but closely related languages: 1) linguistic distances, which include orthographic and phonological distances; 2) surprisal scores obtained from monolingual Language Models (LMs). Our primary objective is to explore the relationship of these two factors with the intelligibility scores and response times of our web-based experiment. Our findings reveal that, while intelligibility scores from the experimental tasks exhibit a stronger correlation with phonological distances, LM surprisal scores appear to be better predictors of the time participants invest in completing the translation tasks.

Microsyntactic units have been defined as language-specific transitional entities between lexicon and grammar, whose idiomatic properties are closely tied to syntax. These units are typically described based on individual constructions, making it difficult to understand them comprehensively as a class. This study proposes a novel approach to detect microsyntactic units using Word Embedding Models (WEMs) trained on six Slavic languages, namely Belarusian, Bulgarian, Czech, Polish, Russian, and Ukrainian, and evaluates how well these models capture the nuances of syntactic non-compositionality. To evaluate the models, we develop a cross-lingual inventory of microsyntactic units using the lists of microsyntantic units available at the Russian National Corpus. Our results demonstrate the effectiveness of WEMs in capturing microsyntactic units across all six Slavic languages under analysis. Additionally, we find that WEMs tailored for syntax-based tasks consistently outperform other WEMs at the task. Our findings contribute to the theory of microsyntax by providing insights into the detection of microsyntactic units and their cross-linguistic properties.

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.

How do neural networks “perceive” speech sounds from unknown languages? Does the typological similarity between the model’s training language (L1) and an unknown language (L2) have an impact on the model representations of L2 speech signals? To answer these questions, we present a novel experimental design based on representational similarity analysis (RSA) to analyze acoustic word embeddings (AWEs)—vector representations of variable-duration spoken-word segments. First, we train monolingual AWE models on seven Indo-European languages with various degrees of typological similarity. We then employ RSA to quantify the cross-lingual similarity by simulating native and non-native spoken-word processing using AWEs. Our experiments show that typological similarity indeed affects the representational similarity of the models in our study. We further discuss the implications of our work on modeling speech processing and language similarity with neural networks.