Linguistic Injustice in Multilingual Technologies: arTenTen and esTenTen as case studies

Establishing English as the
lingua franca in Academia has contributed to what has become known as “linguistic injustice” (see Van Parijs, 2002; Hyland, 2016; Politzer-Ahles et al., 2016; Soler, 2020). This idea states that second-language learners are at a disadvantage when using the new language. The predominance of English and the difficulties that a poor command of the language can pose have been a relevant concern in Digital Humanities (DH) (see Mahony, 2019, p. 384; Galina, 2014, p. 314). When discussing the consequences and possible solutions to the English-speaking bias in DH, the literature focuses on the following problems: i) the lack of translations of research output (see Galina, 2013); ii) issues of connectivity, for example, the so-called “digital divide” (Galina, 2014, p. 314; Mahony, 2019, p. 385); iii) the unavailability of data sources in languages other than English to quantify DH (Galina, 2014, p. 310); iv) problems with digital standards such as Unicode and TEI (see Fiormonte, 2012, pp. 67-69; Mahony, 2019, p. 374); and v) increasing the number of sources of textual information in languages other than English (see Galina, 2014, p. 314). In this sense, multilingual DH critiques seem to be centered on the deficiencies of non-English-language resources rather than on the level of accuracy of digital analytical tools.

The aim of this work is twofold. Firstly, to distinguish a phenomenon that produces a new type of linguistic injustice, which we label as “the paradox of Anglocentric multilingualism.” This paradox arises when a multilingual philosophy is pursued in constructing complex systems of analysis in a digital environment (digital platforms, ontologies). However, these systems imply advantages in the study of English over other languages. The injustice derives from a poor level of precision in the output of technology when analyzing non-English languages. Secondly, we contend that multilingual DH should address the different challenges posed by this paradox. Multilingual DH needs to deal with the deficiencies of tools’ performance as well as those of language resources, because this disadvantage makes it difficult for any cross-linguistic study to provide reliable empirical data in (dis)proving linguistic intuitions.
To illustrate some of the potential problems derived from the paradox, this work will detail the difficulties we have faced in a cross-linguistic study on color terms (Bordonaba-Plou and Jreis-Navarro, forthcoming), when using the Arabic corpus arTenTen (Arts et al., 2014) and the Spanish corpus esTenTen (Kilgariff and Renau, 2013) in Sketch Engine. We will study the different performances of the tool in Arabic and Spanish, compared to English, to point out the weaknesses of this tool in a multilingual arena, making it possible to improve it and enriching the critical and inclusive framework of multilingual DH. Two main issues emerged in our inquiries. Firstly, the lists of collocations provided by the Sketch Word tool are of differing types and usefulness. For example, in Spanish, the tool provides lists like those in English (enTenTen20), i.e., lists based on a functional perspective. However, in Arabic, the researcher has fewer lists available, and those only reflect grammatical categories and the collocation position (left or right). This shortcoming of the tool means it does not provide a complete perspective on the linguistic behavior of the term. Secondly, the analyses conducted by the tool show different degrees of accuracy in Part of Speech (PoS) tagging. In Spanish, the PoS tagger classifies proper nouns as modifiers and verbs. For example, it invents
paular “to paul” (from the proper name Paula),” or
rodrigar “to Rodrigo” (from the proper name Rodrigo).” In Arabic, the PoS tagger does not cover cliticization in smart search. Cliticization is the most important phenomena in Arabic morphology and a big challenge in computational analysis (Habash, 2010, pp. 47-50). For example, the tagger reads
bāhit “pale” as if the prepositional particle proclitic
b+ “with/in” were attached to the non-existent inflected base word
āhit. These inaccuracies imply that the statistical scores –MI-score (Hunston, 2002, p. 71; Baker, 2006, p. 101), t-score (Hunston, 2002, p. 73), and Log Dice (Gablasova, Brezina and McEnery, 2017, p. 164)– cannot be used with total confidence.

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