We focus on the syntactic variation and measure syntactic distances between nine Slavic languages (Belarusian, Bulgarian, Croatian, Czech, Polish, Slovak, Slovene, Russian, and Ukrainian) using symmetric measures of insertion, deletion and movement of syntactic units in the parallel sentences of the fable “The North Wind and the Sun”.
We present an extended version of a tool developed for calculating linguistic distances and asymmetries in auditory perception of closely related languages.
With the emergence of pre-trained multilingual models, multilingual embeddings have been widely applied in various natural language processing tasks.
We further discuss the implications of our work on modeling speech processing and language similarity with neural networks.
In this paper, we present a neural model for Slavic language identification in speech signals and analyze its emergent representations to investigate whether they reflect objective measures of language relatedness and/or non-linguists' perception of language similarity.
State-of-the-art spoken language identification (LID) systems, which are based on end-to-end deep neural networks, have shown remarkable success not only in discriminating between distant languages but also between closely-related languages or even different spoken varieties of the same language.
We report on a web-based resource for conducting intercomprehension experiments with native speakers of Slavic languages and present our methods for measuring linguistic distances and asymmetries in receptive multilingualism.
Languages may be differently distant from each other and their mutual intelligibility may be asymmetric.
Within the first shared task on machine translation between similar languages, we present our first attempts on Czech to Polish machine translation from an intercomprehension perspective.
In an intercomprehension scenario, typically a native speaker of language L1 is confronted with output from an unknown, but related language L2.