Token-level semantic typology without a massively parallel corpus (2025)

Abstract: This paper presents a computational method for token-level lexical semantic comparative research in an original text setting, as opposed to the more common massively parallel setting. Given a set of (non-massively parallel) bitexts, the method consists of leveraging pre-trained contextual vectors in a reference language to induce, for a token in one target language, the lexical items that all other target languages would have used, thus simulating a massively parallel set-up. The method is evaluated on its extraction and induction quality, and the use of the method for lexical semantic typological research is demonstrated.

## introduction lexical semantic typology has benefited immensely from the availability of massively parallel corpora. having the same message translated from a reference language into many different target languages affords linguists a basis to study variation in word meanings (cf. @xcite at the finegrained level of corpus tokens @xcite . the massively parallel set-up (figure 1 , top) allows us to determine, for instance, that spanish and german both split the meaning of english know similarly into 'know someone' (conozco, kenne), and 'know something' (sabe, weiss). studies using massively parallel corpora have challenged prior conceptions of semantic variation, showing that languages vary continuously rather than discretely in where they mark lexical boundaries (e.g., verkerk, 2014 for motion events) and revealing novel factors explaining such lexical boundaries (e.g.,wälchli, 2016 for verbs of visual perception). massively parallel corpora do, however, have methodological downsides (see @xcite for a review). they tend to reflect literary genres and have conceptual content that may be foreign to the culture into whose language the text is translated @xcite . moreover, languages differ in how they habitually formulate, i.e., what conceptual contents speakers typically bring up when they engage in 'the same' linguistic activities, such as telling a story @xcite or making a request @xcite . finally, translated text displays transfer effects, where properties of a source language are transferred to a target language, thus making the target language look more like the source language @xcite , though see @xcite . these issues could in part be circumvented by using original text corpora with translations from the (untranslated) target languages into a shared reference language, as in figure 1 , bottom panel. in comparative linguistics, such corpora are commonly used @xcite . however, under this set-up, the translations are not massively parallel. the loss of massive parallelism impacts the comparability: without it, we can, for instance, no longer directly infer that spanish conozco covers (approximately) the same meanings as german kenne, and ditto for sabe and weiss. moreover, it affects the downstream analytic techniques we can use: many studies rely on dimensionality reduction over the translations of the seed language tokens into all target languages, a situation unavailable with an original text set-up. the use of original text data thus calls for a method to make these data comparable at the token level. multilingual contextualized representation spaces, such as multilingual bert @xcite could be considered here, but given the small amounts of data available for most languages, as well as challenges to the 'true' multilingual nature of multilingual bert @xcite , this approach does not seem feasible. instead, this paper proposes a method that leverages pre-trained contextual vectors for the reference language to induce, for a token in one target language, the lexical items that all other target languages would have used. doing so, the proposed method simulates a massively parallel set-up. pre-trained vectors for one language in a translation pair have been succesfully used to improve word alignment quality in bitexts @xcite , and as such we can expect further translation-oriented applications to similarly benefit from the more substantial training data available for resource-rich languages like english. after describing the method ( §2) and introducing the corpora ( §3), i will report on three experiments validating the method ( §4), and showcase the use of the method for lexical semantic typological research ( §5). code is available at https: //github.com/dnrb/no-parallel-corpus . ## a method for inferring lexification here, i propose a method to simulate a massively parallel setting when such parallelism is not available. the method takes as its input raw bitexts with translations from a target language t into a reference language r for which contextual vectors are available or can feasibly be trained. the method uses these contextual vectors to induce a classification model predicting lexical choice in t. this model can then be applied to translations of another target language t ′ into r, to infer the lexical choice that t would have made if asked to translate a word token in t ′ . doing so for all languages lets us to infer a token-by-language table like those used in studies based on massively parallel corpora. ## experimental set-up and materials this paper uses the doreco corpus @xcite , a collection of data gathered by documentary linguists for a typologically diverse sample of languages. the individual language resources form free-standing contributions that should be individually cited as part of the usage agreement. the structure of the components of the doreco corpora is given in finally, in the induction step, bert @xcite was used, using the bert-base-cased model of the transformers library. ## validation experiments this section validates the quality of the model. as the extraction of high-quality translation equivalence relations between tokens in the target and reference language is paramount for the validity of subsequent steps, i first evaluate the liu et al. (2023) model, which provides us with such translation equivalences, in two ways: by assessing if reference language items are aligned with the correct target language tokens ( §4.1), and by assessing if the extracted 'lemmas' accurately lemmatize the target language ( §4.2). next, i consider the accuracy of the lexification induction step ( §4.3). ## application the previous section demonstrated that the model extracts generally valid target language representations (lemmas) and is reasonably well able to clas-sify these lemmas on the basis of contextual vector representations of the seed language. the goal of this approach, however, is to provide a method for typologists to obtain massively comparable data in the absence of a massively parallel corpus. this section demonstrates how known insights can be replicated, and how novel insights can be obtained with the method. to explore the comparability afforded by the model, here, we briefly explore the domain of visual perception verbs, translation equivalents of english see, look, and watch. a main lexical distinction between experiencer and activity verbs (english see vs. look) -with the former involving a more passive ('experiencing') role for the perceiver, and the latter a more active one has been postulated @xcite , but challenged on the basis of parallel corpus data by @xcite . using manually extracted instances from comparable corpora, san roque et al. ( 2018 ) consider the nonliteral extensions of perception verbs, noting that discourse markers (e.g., look! to draw attention or introduce something unexpected) are common extensions. to explore the distribution of visual perception verbs in the doreco corpus, we can train the best classifier from §4.3 (mlp-100) for each language that has @xmath2, look, and watch) in their free translations. next, we apply this classifier to all instances of see, look, and watch for all other languages, leading to a 3001 (instances of visual perception verbs across all 29 languages with sufficient data) by 194 (unique lemmas across the 37 languages with sufficient data) table, with the probability assigned by each mlp-100 model to the lemmas as the cell values. to visualize this table, we can apply t-sne (van der @xcite to reduce the table to two dimensions. figure 6 shows the t-sne representation, with six distinct colour-codings. the top-left subfigure (6a) shows the distribution of the three english seed words, which form coherent groups of visual clusters, but with each term nonetheless covering multiple clusters. some languages, such as dolgan (6b) do not make any lexical distinctions in this domain -a situation predicted by viberg (1983)), while others, such as sümi (6d) split activity and experiencer meanings more or less along the lines of english. two languages carve out a cluster near the bottom of the 2d-space -cashinahua bena and teop rake -these are all instances of look for, meaning 'search', which many languages group with the other 'look' meanings, but these two languages distinguish lexically. finally, urum (6f) presents an interesting case of two main terms, but with a split that differs from english or sümi. here, we see that bah covers a region containing english look and some of see, whereas gor covers only part of the see tokens. the see tokens covered by bah involve cases of modal see, like can see, will see, in several cases in the meaning 'find out', like "i will see where to go, possibly to the city". as such, urum supports the argument of wälchli (2016) that the activity-experiencer split is (a) more of a continuum, and (b) governed by properties beyond the general semantic role of the perceiver. what the plots in figure 6 further illustrate, is that languages differ in how often they use visual perception meanings. urum uses visual perception verbs only 21 times per 10, 000 tokens, whereas cashinahua shows five times that frequency at 102 tokens per 10, 000. such usage variation is known to be meaningful in the explanation of lexification patterns, following the argument that a language's greater need to communicate about a specific concept correlates with finer-grained lexical distinctions (cf. @xcite . original corpus data and methods for making such data comparable can thus be used to estimate such 'need probabilities' ## conclusion this paper introduced a novel method for making original text corpora that are translated into the same reference language comparable, thus allowing for token-level typological study. the independent steps of the method were found to generally provide high-quality representations in three validation experiments, and the case study presented the potential of the method for studying lexical semantic variation across languages. while generally successful in extracting translation equivalents and inducing lexical categorization models, room for improvement remains. while the @xcite approach benefits from its ability to consider substrings below the word level, it is hampered by not considering how other target language substrings translate to the seed item, something word alignment procedures from ibm-1 @xcite onward do consider. it should be stressed here that using original text does not make the method bias-free, in terms of a translationese bias from the shared reference language. using the free translations means all lexical choice models are filtered through contextual vector representation of english. in the specific case of the data used here, this english is moreover written as a guide for the linguistically informed reader to make sense of the target language sentence; it may, by design given the genre of "free translations in language documentation", show translation effects from the target language onto the english. calibrating the extent of this effect would require further testing the model on other comparable corpora. applications beyond the ones the method was designed for could be explored. related work that considers crosslinguistic variation at a word type level and using secondary resources, like thompson et al. (2020) and khishigsuren et al. (2025) , could be compared against the token-level mappings between a shared reference language and multiple target languages. corpora that contain both original and translated text in comparable genres may furthermore be of use to pinpoint the precise effects of translationese in how lexical boundaries are drawn, and as such be of use for practical purposes in education and translation studies. finally, we are reminded that languages vary on a discoursepragmatic level, and that multilingual nlp ought to consider such variation, for instance when working with large language models and machine translation systems pretrained on translated text. michael franjieh. 2024. fanbyak doreco dataset. in frank seifart, ludger paschen, and matthew stave, editors, language documentation reference corpus (doreco) 2.0. laboratoire dynamique du langage (umr5596, cnrs & université lyon 2), lyon. alexandro garcia-laguia. 2024. northern alta doreco dataset. in frank seifart, ludger paschen, and matthew stave, editors, language documentation reference corpus (doreco) 2.0. laboratoire dynamique du langage (umr5596, cnrs & université lyon 2), lyon. jost gippert. 2024. svan doreco dataset. in frank seifart, ludger paschen, and matthew stave, editors, language documentation reference corpus (doreco) 2.0. laboratoire dynamique du langage (umr5596, cnrs & université lyon 2), lyon. richard griscom. 2024. asimjeeg datooga doreco dataset. in frank seifart, ludger paschen, and matthew stave, editors, language documentation reference corpus (doreco) 2.0. laboratoire dynamique du langage (umr5596, cnrs & université lyon 2), lyon. valentin gusev, tiina klooster, beáta wagner-nagy, and alexandre arkhipov. 2024. kamas doreco dataset. in frank seifart, ludger paschen, and matthew stave, editors, language documentation reference corpus (doreco) 2.0. laboratoire dynamique du langage (umr5596, cnrs & université lyon 2), lyon. tom güldemann, martina ernszt, sven siegmund, and alena witzlack-makarevich. 2024. nng doreco dataset. in frank seifart, ludger paschen, and matthew stave, editors, language documentation reference corpus (doreco) 2.0. laboratoire dynamique du langage (umr5596, cnrs & université lyon 2), lyon. dialectology, typology, and register analysis: linguistic variation in text and speech, pages 324-354. åke viberg. 1983. the verbs of perception: a typological study. alexandra vydrina. 2024. kakabe doreco dataset. in frank seifart, ludger paschen, and matthew stave, editors, language documentation reference corpus (doreco) 2.0. laboratoire dynamique du langage (umr5596, cnrs & université lyon 2), lyon. bernhard wälchli. 2016. non-specific, specific and obscured perception verbs in baltic languages. baltic linguistics, 7:53-135. claudia wegener. 2024. savosavo doreco dataset. in frank seifart, ludger paschen, and matthew stave, editors, language documentation reference corpus (doreco) 2.0. laboratoire dynamique du langage (umr5596, cnrs & université lyon 2), lyon. søren wichmann. 2024. texistepec popoluca doreco dataset. in frank seifart, ludger paschen, and matthew stave, editors, language documentation reference corpus (doreco) 2.0. laboratoire dynamique du langage (umr5596, cnrs & université lyon 2), lyon. alena witzlack-makarevich, saudah namyalo, anatol kiriggwajjo, and zarina molochieva. 2024. ruuli doreco dataset. in frank seifart, ludger paschen, and matthew stave, editors, language documentation reference corpus (doreco) 2.0. laboratoire dynamique du langage (umr5596, cnrs & université lyon 2), lyon. xianming xu and bibo bai. 2024. sadu doreco dataset. in frank seifart, ludger paschen, and matthew stave, editors, language documentation reference corpus (doreco) 2.0. laboratoire dynamique du langage (umr5596, cnrs & université lyon 2), lyon.