Known Words Will Do: Unknown Concept Translation via Lexical Relations (2022)

Abstract: Translating into low-resource languages is challenging due to the scarcity of training data. In this paper, we propose a probabilistic lexical translation method that bridges through lexical relations including synonyms, hypernyms, hyponyms, and co-hyponyms. This method, which only requires a dictionary like Wiktionary and a lexical database like WordNet, enables the translation of unknown vocabulary into low-resource languages for which we may only know the translation of a related concept. Experiments on translating a core vocabulary set into 472 languages, most of them low-resource, show the effectiveness of our approach.

## introduction when humans encounter lexical gaps in their speech, they may attempt to "talk around" it -a process known as circumlocution -or use another known, related word such as a synonym. similarly, in machine translation (mt), one method for resolving out-of-vocabulary words (oovs) involves replacing them with synonyms from the known lexicon. synonym replacement is especially useful in a low-resource setting and has been recently investigated, for example in vietnamese @xcite and japanese @xcite . some mt evaluation metrics also use synonyms as part of their computation (banerjee and lavie, @xcite . other applications of synonyms include improving robustness of mt systems @xcite , finding translations in comparable corpora @xcite , and improving information retrieval systems @xcite . however, synonyms are not the only lexical relation through which translations can be found. for example, the concept of watermelon can be translated in serbo-croatian as бостан 'melon' (a hypernym) and in italian as cocomero 'cucumber' (a co-hyponym). these lexical relations have not been adequately studied in the literature as sources for translation. translation via lexical relations are usually studied in the context of constructing multilingual wordnets @xcite , where researchers translate the english wordnet in order to bootstrap the construction of a new wordnet in their target language. in contrast, our work investigates the acceptability of a word's translation in a lowresource language based on lexically-related concepts across multiple languages. our work is related to the idea of translation bridging @xcite , where a word in the source language is first translated into an intermediate bridge language, then translated into the target language. however, instead of bridging through a third language, we propose bridging through lexically-related words in the same language. we specifically focus on four types of lexical semantic relations: synonymy, hypernymy, hyponymy, and co-hyponymy. using the aggregation of these translations across hundreds of languages available in wiktionary in wiktionary, we develop and analyze a probabilistic model of lexical relation bridging to enable the translation of unknown concepts using existing known words in the target language's lexicon. code and data for this paper are available at github.com/wswu/ bridging-lexrel. ## translation bridging via lexical relations suppose we wish to translate into a low-resource language a concept, such as hound, whose translation we do not know in said language. this is quite common in extremely low-resource scenarios, where little to no bitext exists for training machine translation systems, nor is there even any monolingual text for applying unsupervised machine translation methods such as cross-lingual embeddings. this scenario is more common than one might imagine. the world has around 7,000 languages, but roughly 160 of them have readily available bitext or monolingual text, which might be acquired from the web using methods such as paracrawl @xcite or common crawl @xcite . beyond this range, we enter the territory of low-resource languages, where the only significant source of text is likely to be the bible, available for roughly 1,600 languages @xcite . beyond this, the best one can hope for is a small bilingual dictionary perhaps manually constructed by a field linguist or a native informant. what kind of translation is possible with no other bilingual resource but a small dictionary? in english, the word hound is usually used to indicate a hunting dog, so one might intuitively talk about their dog instead of their hound. although dog may not capture the full semantic nuances of hound, it at least conveys the notion that the word it replaces, hound, is a four-legged canine. moreover, it is more likely that the word dog exists in any given dictionary than hound; hound is a more specialized word and thus ranks lower in terms of coreness (see @xcite for one definition of core vocabulary). thus we can replace a less core word with a more core word. the replacement word could be a hypernym, such as dog for hound, foot_0 but could also be a synonym, hyponym, or even a co-hyponym. these four lexical relations are illustrated in the lexical relation graph in figure 1 , using the concept of hound. foot_1 synonyms share the same meaning. hyperynms and hyponyms comprise the isa relation, where the hypernym is the supertype (e.g. melon) and the hyponym is the subtype (e.g. watermelon). co-hyponyms are words that share the same hypernym. in order to obtain lexicallyrelated words, we use wordnet 3.0 @xcite , a freely-available lexical database of english words and their relations. because these relationships are stored in wordnet at the synset level, rather than at the word level, a pair of words may be linked by more than one relation. for example, dog is both a synonym and a hypernym of hound. to develop a model of translations of related in total, this process learns translation distributions for over 42k concepts from 2.4 million relation pairs. as shown in results on languages with over 100 word coverage of the core vocabulary are presented in figure 3 . because our translation model provides a probability for each hypothesis, we report 1-best accuracy (is the top hypothesis in the gold translations?) and 10-best accuracy (are any of the top 10 3 although we have wiktionary translation data for over 4,300 languages, the majority of these are extremely lowresource. evaluating translation via lexical relations requires that we have ground truth for the translation for the related word. thus, for testing purposes, we limit our analysis to languages for which we have at least 100 words of ground truth. hypotheses in the gold translations?). in addition, we evaluate groups of languages by their coverage of the core vocabulary to test the effectiveness of lexical relation translations at various levels of language resourceness. figure 3 presents a high-level summary of this translation approach's performance. we find that for 43 high resource languages (with over 900 word coverage of the core vocabulary), a 1-best accuracy of 36% and a 10-best accuracy of 64% shows that almost 2/3 of concepts can be translated using a lexically-related concept. for lowresource languages that cover at least 100 concepts of the core vocabulary, a respec ## analysis to more deeply understand bridging through lexical relations, we analyze our translation approach in depth, focusing on four test languages, bulgarian, irish, galician, and maltese. detailed results on these languages are shown in we first explain why one should consider we now examine some model predictions in detail. the model was able to apply the lexical relations @xmath0, cure, antidote}, for which we have known translations, allowing the model to produce an appropriate translation of remedy's hypernyms, hyponyms, co-hyponyms, and synonyms. for bulgarian ( galician ( finally, for maltese ( in addition to these experiments, we also examined the effects of training on only languages in the same language family as the test language, versus training on the entire test set. we find that performance is worse when trained on all languages, for bulgarian, galician, and maltese. only for irish did the performance increase. ## conclusion using only the existing lexical resources wiktionary and wordnet, we develop a probabilistic method for accurately predicting the translation of unknown words by bridging through lexically related hypernyms, hyponyms, co-hyponyms, and synonyms. this simple but effective method that identifies existing known words as valid translations does not require any neural model nor intensive training, and is especially well-suited for extremely low-resource languages for which little resources are available. future work will augment our lexical resources with other wordnets and dictionaries, and apply our method to complement existing low-resource translation systems.