Using Linguistic Typology to Enrich Multilingual Lexicons: the Case of Lexical Gaps in Kinship (2022)

Abstract: This paper describes a method to enrich lexical resources with content relating to linguistic diversity, based on knowledge from the field of lexical typology. We capture the phenomenon of diversity through the notion of lexical gap and use a systematic method to infer gaps semi-automatically on a large scale, which we demonstrate on the kinship domain. The resulting free diversity-aware terminological resource consists of 198 concepts, 1,911 words, and 37,370 gaps in 699 languages. We see great potential in the use of resources such as ours for the improvement of a variety of cross-lingual NLP tasks, which we illustrate through an application in the evaluation of machine translation systems.

## introduction to address the language technology bottleneck beyond a handful of well-supported languages, the computational linguistics community has proposed several solutions: unsupervised learning models that remove the need for parallel textual corpora @xcite @xcite or cross-lingual transfer from high-to low-resourced languages @xcite @xcite . joint supervised learning and representation learning also happen to be effective in certain multilingual nlp applications, e.g. neural machine translation @xcite . the common point of these methods is that they rely on an implicit assumption of sameness across languages: for example, that in cross-lingual transfer the lexicon of one language can be efficiently mapped to that of another language through a shared semantic vector space. while the existence of shared cross-lingual conceptualizations is obvious to all-otherwise no interlingual communication could ever be possible-the world's languages are, however, also known to be extremely diverse on every level @xcite . thus, in recent years there has been an emerging trend to exploit results from the field of linguistic typology into language diversity in various multilingual nlp tasks (for a review, please see @xcite and @xcite ). typology-informed nlp studies use typological features from hand-curated resources, such as the world atlas of language structures (wals) @xcite , to bind parameters of languages that have similar typological features within the framework of cross-lingual transfer @xcite or to manipulate joint supervised learning models to reflect typological features of specific languages @xcite . the use of cross-lingual cognate databases for bilingual lexicon induction was explored by @xcite . the use of typology in various nlp applications generated consistent improvement, and is thus regarded as an effective way to advance the development of multilingual nlp @xcite @xcite . most typology-informed nlp studies, however, are limited to recognizing language-specific morphosyntactic features and have so far ignored diversity within lexicons. yet, the vocabularies of languages differ considerably in their division of the semantic space. for example, many languages lack an equivalent to the english word cousin, and instead employ several (up to 16) more specific words that distinguish male and female cousins, elder and younger, paternal and maternal, etc. the ignorance of such semantic diversity in language processing can lead to hard-to-detect meaning-level mistakes. today's top machine translation systems, for instance, make consistent mistakes over simple sentences such as my brother is younger than me even across relatively high-resource languages: for example, the japanese translation of the english sentence above is 私の 兄は私より若いです with a nonsensical meaning of my elder brother is younger than me. the same mistake is observed over many other languages such as mongolian or hungarian. it is clear from the example above that manifestations of language diversity are only revealed explicitly in a crosslingual context, and remain hidden as long as one's point of view remains monolingual. hence, the way to formalise lexical diversity is through building typology-informed multilingual lexical resources. in this paper we exploit existing knowledge from lexical typology to enrich and extend existing multilingual lexicons which, in turn, can be reused in nlp applications. the key notion to capture lexical diversity is that of the lexical gap, which refers to the lack of lexicalization of a particular concept in a particular language. we provide a formal approach to infer lexi-cal gaps semi-automatically from existing domain-specific lexical knowledge. we apply the method to the domain of kinship, well known for its cross-lingual diversity, building a resource that provides 37,370 gaps in 699 languages. finally, as an example application in the context of multilingual nlp, we demonstrate how our resource can be used to evaluate machine translation systems over semantically challenging corpora. to our knowledge, ours is the first systematic method and the first lexical resource that provides lexical gaps for multiple languages in a large-scale and exhaustive manner. the rest of the paper is organized as follows. we start by defining the notion of lexical gap and giving a brief review of lexical typology in section 2. our typology-based lexical gap generation method is described in section 3. section 4 evaluates generation results based on native speaker input. sections 5 and 6 present the resulting dataset and an example application for improving machine translation. finally, we review related work and provide conclusions in sections 7 and 8. ## untranslatability and lexical typology the notion of lexical gap is closely related to that of untranslatability @xcite . the latter, however, is a practically-oriented concept with many possible definitions: the acceptability of a translation depends on how rigorous one is regarding precision, conciseness, register, style, etc. accordingly, the notion of lexical gap can be defined in broader or narrower ways. linguists tend to adopt a more strict definition where monomorphemic words for which an equally monomorphemic translation cannot be provided are considered as gaps. for example, @xcite considered that the concept of "color" is a lexical gap in warlpiri, an australian indigenous language, as it lacks a word for it. another example from @xcite , the general concept of "rice" is a lexical gap in korean, which instead has words for more specific concepts "cooked rice" and "uncooked rice". in a computational context, bentivogli and @xcite adopt a somewhat more relaxed definition, distinguishing translations into single words and restricted collocations on the one hand, and free combinations of words on the other hand, considering only the former as valid lexicalizations. under this account, a restricted collocation is a stable combination of words (e.g., "elder brother"). on the other hand, a free combination of words is a combination of words that are not bound together, and its parts can be freely used with other lexical items (e.g., "father's elder brother"). in our research, we adopted the definition of @xcite and, accordingly, considered a concept to be a lexical gap in a language if it can only be expressed through a free combination of words. another criterion was only to consider as lexicalizations general-language words understood by "average" native speakers, as opposed to specialized terminology or rare and unknown words. for example, the genderindependent english nibling and french adelphe are specialized terms that designate nephew or niece and sibling, respectively. as these neologisms are only understood by specialists and are never used by the general public, we considered them as lexical gaps. lexical typology, a field of linguistics, explores systematic variations in the presence or absence of lexicalizations in languages with respect to specific domains (also known as "domain categorization") @xcite . english and northern sami, for example, categorize unclelike relationships differently: northern sami has three different words with three distinct meanings-eahki "father's elder brother," čeahci "father's younger brother," and eanu "mother's brother"-while english packs all these meanings into the single term uncle. studies in lexical typology have mostly been conducted on domains that offer an unexpected richness of cross-lingual diversity: body parts, color, kinship, perception verbs, motion events, spatial dimension terms, cardinal direction terms, cutting and breaking events, putting and taking events, or pain predicates (for a review, see koptjevskajatamm et al. (2015) and arora et al. (2021) ). however, only a small part of related scientific results have been published as actual datasets: murdock (1970)'s kinship categorization is published in d-place @xcite . parts of @xcite and @xcite 's works on colors are published under the lexicon chapter of wals. to the best of our knowledge, no other works have been published as open resources. more recently, digital lexical resources and parallel corpora have been increasingly used in lexical typology, enabling typologists to cover more languages and domains. for example, viberg (2014)'s 50-language study on the perception domain was extended to 1,220 languages in georgakopoulos et al. @xcite . a study of the color domain in 119 languages by @xcite was leveraged in @xcite , which managed to cover 2,491 languages. recent automated attempts to obtain lexicosemantic knowledge from large-scale parallel corpora provided promising results as well @xcite . such bottom-up, datadriven approaches extend existing knowledge on lexical typology which, in turn, broaden the applicability of our method. ## lexical gap generation method in this section, we describe a top-down, linguistically informed method for the systematic generation of lexical gaps for a large number of languages. while the method is generic and can be applied to multiple domains, our efforts so far have concentrated on the domain of kinship relations, from which all of our examples are taken. the method consists of the following three steps: ## evaluation our evaluations verified the correctness and completeness of the automated word extraction and gap generation method described in section 3, and also extended the overall coverage of our resource. our goal was to obtain highly reliable and reusable data on lexical diversity, we thus gave priority to precision over recall. furthermore, as we considered our sources from linguistic typology as a priori reliable, we instead focussed our evaluations on the words and gaps that were automatically inferred from wiktionary data. thus, we verified the correctness of: ## the published resource based on evaluation results, we considered the precision of our gap inference rules to be high enough to be applied to the final dataset without any modification. the kinship database has also been imported into the universal knowledge core (ukc) foot_6 , a multilingual lexical database of more than 1,000 languages @xcite . the focus of the ukc is language diversity: it is capable of representing lexical gaps and its online version is equipped with interactive visualisation tools that allow the browsing of kinship terms and gaps by subdomain in all supported languages @xcite , as shown in figure 2 . ## application in machine translation we believe that resources on lexical gaps have a high potential in improving existing cross-lingual applications, for example as pivots in multilingual representation learning, as seeds in cross-lingual model transfer, and also as part of multilingual word embeddings. in this section, we demonstrate how our resource can be used to improve and evaluate machine translation systems. ## related work the large-scale multilingual lexical databases that exist today have been, by and large, produced and used by two distinct communities of researchers, namely historical and computational linguists. the former community has produced the intercontinental dictionary series (ids) (key and comrie, 2015), the automated similarity judgement program (asjp) @xcite , clics @xcite , cldf @xcite , and the world loanword database (wold) @xcite . these resources typically consist of phonemic descriptions of words or transliterations, as modern orthographies are irrelevant to both comparative and historical linguistics. this characteristic, however, makes these resources difficult to use in computational applications that target contemporary written language. the computational linguist and nlp communities, on the other hand, rely on resources derived from and describing contemporary written language. formal, computerprocessable lexical databases, such as babelnet @xcite , the open multilingual wordnet (omw) @xcite , or concepticon @xcite , however, focus on representing sameness, i.e. word meanings shared across languages, and do not explicitly indicate untranslatability. in babelnet and omw, language-specific word meanings either are left out or are mapped to other languages in an approximative manner. such inaccuracies lead to a western-centric bias as the word meanings that are correctly mapped belong to dominant languages such as the english princeton wordnet @xcite . we are aware of two efforts that address the formal representation and methodology of building diversity-aware lexical databases: multiwordnet (mwn) @xcite and the announced second version of the open multilingual wordnet (omw2) @xcite . mwn has inbuilt support for representing lexical gaps and provides about 300 and 1,000 lexical gaps in hebrew @xcite and italian @xcite , respectively. the recent paper @xcite announced that omw2 would be based on the collaborative interlingual index (cili), which envisions a collaborative method for defining language-specific concepts and gaps. we are not yet aware of the availability of an actual resource that would correspond to the theoretical diversityaware representational abilities of the cili. as also put forth in @xcite and @xcite , identifying lexical gaps in a systematic manner is far from trivial. let us take the example of the english word cousin which has no equivalent in hindi and the hindi word चचे रा भाई meaning son of father's brother which, in turn, has no concise equivalent in english. the hindi gap corresponding to cousin can be identified as part of a expert-driven lexicon translation effort-called expansion in @xcite -that is frequently used to produce lexicons for relatively lower-resourced languages. this approach was used, for example, to provide around 600 gaps in the unified scottish gaelic wordnet @xcite and 79 gaps in mongolian wordnet @xcite . this method, however, is effort-intensive and does not provide any gaps in the reverse direction. traditional bilingual dictionaries that explicitly indicate untranslatability may be a good source of gaps, which corresponds to the merge method used in mwn. such high-quality dictionaries, however, are not available for lots of languages. our approach is different from both of these: instead of the entire lexicon, it focuses on a single domain that is well-known to be cross-lingually diverse, such as kinship, colors, or body parts. instead of human experts or existing lexical resources, it relies on evidence from linguistic typology. as a result, in the given domain it provides a much more exhaustive coverage both in terms of the num-ber of gaps per language and in terms of the number of languages covered. lastly but perhaps most importantly, ours is a predominantly top-down method that, thanks to knowledge from linguistic typology, is based on a prior conceptual understanding of the domain at hand (e.g. for the kinship domain, the analysis of 162 languages by @xcite ). the result is that we are able to construct a hierarchy of interlingual lexico-semantic domain concepts with considerable precision, with language-specific lexicalisations (or the lack thereof) easily mappable to the hierarchy for lots of languages. in contrast, the bottom-up expand and merge methods, envisaged for the cili and used in mwn, proceed by gradually extending an english-specific hierarchy, as new languages and new concepts are "encountered". this leads to the need for a (sometimes non-monotonic) reorganisation of the concept graph as the knowledge about a given domain evolves based on cross-lingual evidence. as shown in figure 1 , the one-by-one addition of mongolian, greenlandic, and korean leads to profound changes in the interlingual representation. ## conclusion and future work our paper and the corresponding resource aim to address a gap in current multilingual lexicons and cross-lingual applications, namely the representation and exploitation of lexical diversity. we formally capture diversity through the notions of language-specific concepts and lexical gaps, and provide a systematic method to produce such data in a semiautomated manner. our first large-scale effort focused on the domain of kinship terminology, well known to be particularly diverse across languages and cultures. the resulting machine-readable resource provides a wide coverage of domain concepts and languages (198 kinship concepts covered in 699 languages), and is freely available both for online browsing and download. in the future, we plan to apply the method presented in this paper to formalize new domains that are known to be diverse, such as colors, food, or visual objects @xcite . one such ongoing project concerns culturally specific concepts in the languages of india @xcite . finally, we believe that resources such as ours provide essential information to lexically-focused cross-lingual applications, such as multilingual language models or cross-lingual transfer. we have presented one such application in the context of machine translation, but we plan to explore other application areas in the future. we also plan to extend our machine translation experiments to additional state-of-the-art mt systems.