It takes two to borrow: a donor and a recipient. Who’s who? (2024)

Abstract: We address the open problem of automatically identifying the direction of lexical borrowing, given word pairs in the donor and recipient languages. We propose strong benchmarks for this task, by applying a set of machine learning models. We extract and publicly release a comprehensive borrowings dataset from the recent RoBoCoP cognates and borrowings database for five Romance languages. We experiment on this dataset with both graphic and phonetic representations and with different features, models and architectures. We interpret the results, in terms of F1 score, commenting on the influence of features and model choice, of the imbalanced data and of the inherent difficulty of the task for particular language pairs. We show that automatically determining the direction of borrowing is a feasible task, and propose additional directions for future work.

## introduction and related work lexical borrowing is an essential witness to sociocognitive and cultural evolution of mankind. beside its immediate implications in linguistic phylogeny @xcite @xcite , lexical borrowing, as primary evidence for contact between different communities, can be used as a source on history (heggarty, 2015), anthropology @xcite , or sociology @xcite , providing important clues to linguistic changes in the past @xcite . while offering significant proofs of migration or geographic expansion @xcite , the lexical data attesting linguistic contact corroborate the archaeological inventory (cf. @xcite ) and provide the basis for 'linguistic paleontology' or 'socio-cultural reconstruction' @xcite . while there is extensive literature @xcite @xcite @xcite on the related problem of cognate identification both in classical and computational linguistics, automatic borrowing detection is much less studied @xcite . in our present study we approach the new task of automatic borrowing direction detection. determining the direction of lexical borrowing -still one of the open questions in historical linguistics @xcite -is crucial for an accurate inference of information for the above-mentioned domains: given a pair of languages, l 1 and l 2 , and a word that is present in a very similar form in both languages, without it being inherited in one or both, and with no traces of a third language from which it could have entered in both l 1 and l 2 , the question that arises in not a few cases is which one of the two languages is the donor. the correct identification of the borrowing direction helps us decide which community had primacy over a certain object or concept (setting thus the basis of an archaeology of concepts), just as it enables us to trace back the type of social interaction among groups, by revealing the dominant language (cf. @xcite ) as donor of core borrowings @xcite . pointing out the donor language of a certain loanword is also vital for a correct etymological analysis, narrowing the track for the search of the word's origin. lexical borrowing has been studied from several points of view: leaving aside the traditional approaches which focused mostly on the phonemic or morphological adaptation of loanwords @xcite , more recent -still classical -approaches either aim at detecting the semantic areas which are more permeable to borrowings @xcite , or propose an interpretation of the socio-economical relations between languages based on the lexical reflection of their linguistic contact @xcite . the new research methods brought in by computational linguistics have shed a new light on the issue @xcite , while the search for an automatic procedure to be used in the analysis of borrowings has led to breakthrough works that address difficult problems like the distinction between borrowings and virtual cognates @xcite @xcite , determination of monolexical borrowings @xcite , computational etymology of borrowings @xcite or discrimination between inherited and borrowed words from the same source language @xcite . however, to the best of our knowledge, the possibility of determining the direction of borrowing has not yet been assessed. this problem has various facets and implications. it is not infrequent the case in which a word that exists simultaneously in l 1 and l 2 is registered in the etymological dictionaries of l 1 as coming from l 2 and in the dictionaries of l 2 as a borrowing from l 1 . the difficulty of identifying the direction of borrowing is not limited to ancient, non-documented languages, or to under-studied linguistic families. even in extensively researched linguistic domains, like the romance languages family, we may still encounter lexicographical controversies regarding the donor-recipient relationship. for instance, a long-standing etymological controversy concerns the origin of sp. tacaño / it. taccagno 'stingy', attributed by the italian dictionaries to spanish (deli2, mauro 1 ) and vice-versa (dle); the same situation emerges for sp. tozo 'dwarf', interpreted as a borrowing of it. tozzo 'thick and short' (dle), while it. tozzo is labeled as borrowed from sp. tozo (deli2). 2 the traditional approach to such uncertainties (cf. @xcite ) could only make use of two the approach we propose is only a first step forward, while trying to meet the "challenge" posed by @xcite : "whether it will be possible to identify even the direction of borrowings, when developing these methods further, is an open question." starting with these remarks, our main contributions are: we also investigate whether graphic or phonemic similarities between words are more relevant. the rest of the paper is organized as follows. in section 2 we present the database that we use, and offer details about the processing steps involved, while in section 3 we introduce our approach for the automatic detection of borrowing direction and present the experiments. extensive results and error analyses are presented in section 4. the last section is dedicated to final remarks and future works. ## data to obtain a dataset of borrowings for the five romance languages (romanian, italian, portuguese, french, and spanish), we used the robocop cognate and borrowing database @xcite . our choice is motivated by its quality and highcoverage. since the sources of this database are dictionaries of five romance languages, robo-cop has a wide coverage, including all the words currently in use for five languages. it is one of the most comprehensive, inclusive, and complex databases of romance related words. moreover, because it was obtained in a computer-assisted manner and manually checked, it minimizes the noise and is reliable, in contrast to other resources created from wiktionary @xcite or from automated translations @xcite . based on the etymologies available in robocop, we extracted lists of borrowed words across five romance languages, for each language pair (the database will be available for research purposes upon request). formally, for any triplet in the robocop @xmath4, e, l 1 > where u is a word in romance language l 1 and e is its etymon, for which e is a word in a romance language l 2 , we add the @xmath5, @xmath6. we distinguish between words borrowed by language l 1 from language l 2 and the words borrowed by language l 2 from language l 1 . we thus obtain twenty lists of borrowed words, for each ordered language @xmath7, l 2 >, where l 1 and l 2 are distinct languages in the set of the five romance languages considered. to the best of our knowledge, these are the most comprehensive lists of borrowings for the five romance languages publicly available in digital format. as a direct result of using the robocop database, the borrowings in our dataset are cleaned of potential noise resulted from the data collection process, but they preserve the accents, diacritics and any other characters that are part of the orthography of the words and etymons. the only cleaning operation we perform on the graphic form of the words is to remove the accents that mark the stress of the word, but are not part of the orthography of the language and, therefore, do not represent relevant information for our task. the phonemic representation is left as it is foot_0 . ## experiments and methodology in this section we report experiments on our dataset containing the five romance languages, by training several models to automatically recognize the direction of borrowing, given a pair of words in two languages where one was borrowed from the other. given a language pair (l 1 , l 2 ) and a set of word pairs the task is to determine the actual direction of borrowing for any given pair (w 1 , w 2 ). we use two main approaches: ensemble of machine learning classifiers and transformer based models. in all scenarios, we train bilingual models that learn to identify the borrowing direction for a given pair of languages. we experiment with both the graphic and phonemic representations of the borrowings and with different types of features, which are described in detail below. note that, the order in which the words from a pair are fed to these models is constant across training and evaluation (e.g. for italian-french, the italian word always precedes the french word in the example pairs). these orders were chosen arbitrarily, but because of the symmetry of the proposed approaches, they should not affect the results in any way. ## results and error analysis in this section we present the main results obtained and we perform an in-depth analysis of the results and errors. ## conclusions we obtained the first results, to the best of our knowledge, for the task of automatically predicting the direction of borrowing between pairs of words in four romance languages (italian, spanish, portuguese, and french), based on orthographic and phonemic features. at this stage, we designed the experiments specifically to limit the input to the models to only word forms, in order to understand how well machine learning can work for our purpose in this setup. our results show that the task can be solved with machine learning with high performance (obtaining perfect predictions on the test set for certain language pairs), and that results can vary depending on the language pair. the average f1 score for the best models for all the 6 pairs of romance languages analyzed was 85.4% the challenge of this task formulated in this way is specifically to understand whether the problem can be solved without any additional information. the promising results already obtained show us that we can hope for even better success in identifying the direction of borrowing by including new features, such as chronological parameters (the oldest record of the words in the two languages), cultural ones (in which culture does the concept fit better), or semantic ones @xcite . the semantic criterion we expect to have an important effect on classification performance, given that, in cases of polysemous words, the number of meanings can elucidate the antiquity of a word in a given language: a rich semantic expansion of a lexeme in l 1 versus a poor or absent polysemy in l 2 is more eloquent than simple phonemic features. at a technical level, jointly learning some of the parameters for words in each language across language pairs could improve the models' capacity it es pt fr it @xmath8, t->tt, l->li @xmath9, z->ss, @xmath10, @xmath11, -@xmath12, et->ett, ll->gli @xmath13, @xmath14, ->en @xmath15, @xmath16, @xmath17, az->ass, @xmath18, @xmath19, @xmath0so @xmath20, @xmath21, @xmath22, ta@xmath1, @xmath2es @xmath23, @xmath24, @xmath25, @xmath3-, @xmath26: top 3 informative graphic alignment bigrams and trigrams according to χ 2 feature selection. n-grams are separated by commas, > marks where the n-gram for the first word in the pair ends and where the n-gram for the second word begins, -marks an insertion/deletion computed by the alignment algorithm. bigrams are shown above the main diagonal, whereas trigrams are shown below it. to encode the linguistic structures, and could help improve performance for language pairs with less training data.