This special issue of the Nordic Journal of Linguistics is devoted to receptive multilingualism (RM), and includes papers presented in the conference Receptive Multilingualism: Multilingual Resources in the Service of Mutual Understanding, held at the University of Eastern Finland, Joensuu, in 2015. RM refers to a language contact situation in which hearers’ or readers’ understanding of a non-native language is based on their skills in either their first language or languages that they have learned later in life. In such situations comprehension can be based on linguistic similarity of the languages, on acquired knowledge of the languages, or both. A particular characteristic of RM is its emphasis on receptive language skills: the hearer or the reader does not necessarily actively command the language they are able to understand. In its most interactive form RM refers to a situation in which every conversation participant can use their native language and be understood by the others.

We report on a large-scale investigation of the mutual intelligibility between five Germanic languages: Danish, Dutch, English, German and Swedish. We tested twenty language combinations using the same uniform methodology, making the results commensurable for the first time. We first tested both written and spoken language by means of cloze tests. Next we calculated linguistic distance at the levels of lexicon, orthography, phonology, morphology and syntax. We also quantified exposure and attitudes towards the test languages. Finally, we carried out a regression analysis to determine the relative importance of these linguistic and extra-linguistic predictors for the mutual intelligibility between Germanic languages. The extra-linguistic predictor exposure was the most significant factor in predicting intelligibility in the Germanic language area. The effect of attitude was very small. Lexical distance, orthographic and phonetic distances were the most important linguistic predictors of intelligibility.

The distance or similarity between two languages can be objective or actual, i.e. discoverable by the tools and methods of linguists, or perceived by users of the languages. In this article two methods, the Levenshtein Distance (LD), which purports to measure the objective distance, and the Index of Perceived Similarity (IPS), which quantifies language users’ perceptions, are compared. The data are the quantitative results of a test measuring conscious perceptions of similarity between Estonian and Finnish inflectional morphology by Finnish and Estonian native speakers (‘Finns’ and ‘Estonians’) with no knowledge of and exposure to the other (‘target’) language. The results show that Finns see more similarity between Finnish and Estonian than Estonians do. Also the correlations between LD and the perception results of the Finns are statistically significant while the correlations between the LD and the IPS scores of the Estonians are not. Comments by test participants provide insights into the nature of the perceptions of similarity.

Focusing on orthography as a primary linguistic interface in every reading activity, the central research question we address here is how orthographic intelligibility can be measured and predicted between closely related languages. This paper presents methods and findings of modeling orthographic intelligibility in a reading intercomprehension scenario from the information-theoretic perspective. The focus of the study is on two Slavic language pairs: Czech–Polish (West Slavic, using the Latin script) and Bulgarian–Russian (South Slavic and East Slavic, respectively, using the Cyrillic script). In this article, we present computational methods for measuring orthographic distance and orthographic asymmetry by means of the Levenshtein algorithm, conditional entropy and adaptation surprisal method that are expected to predict the influence of orthography on mutual intelligibility in reading.

Several factors affect the comprehension of a text written in a language related to the reader's first language (L1): (i) orthographic similarity with the reader's L1, (ii) contextual clues, (iii) semantic relationships between components of phraseological units, and (iv) L1 reading comprehension strategies. This article compares the results of a cloze test (CT), in which a group of Finns read a text in their L1 (Finnish) and filled the gaps, and a translation test (TT), in which another group of Finns translated the Estonian version of the same text into Finnish. This text included five pairs of primes and targets, parts of the same phraseological unit, representing different semantic relations; in the CT the target was replaced by a gap and in the TT the respondents had to translate the target. The results indicated that the respondents used similar inferencing strategies in both tests, and thus provide evidence for the assumption that orthographic similarity is not the sole factor contributing to understanding a foreign text, but that L1 reading comprehension strategies are also employed.

The study of crosslinguistic influences (CLI) has proven that morphosyntactic features exhibit CLI. Technical development and novel resources have enabled detection-based approaches, where potential CLI are revealed based on their observed frequencies and on differences between learners with different language backgrounds. The two research questions are as follows: (i) How construction-specific typological (dis)similarities between L1 and L2 affect the frequencies of linguistic features? (ii) Can such (dis)similarities be detected by comparing feature frequency data of L2? The data come from the International Corpus of Learner Finnish, and the methodology applied is the key structure analysis. The results support the applicability of the method: they show that constructional similarities may trigger CLI construction by construction, irrespective of the general similarities or genealogical categorizations. The results further imply the importance of controlling the genre-related and topical variation to account for skewed nature of the data when dealing with naturally occurring learner language data.