Challenge: a new method to expand the knowledge in existing dictionaries is proposed . small Uralic languages are facing a problem of limited language resources .
Approach: They propose to combine conceptually divided translations from multilingual dictionaries for small Uralic languages into a single lexical entry.
Outcome: The proposed method can be used to expand existing dictionaries and provide translations when adding new entries.

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Challenge: Existing approaches to cross-lingual vocabulary transfer face challenges when dealing with low-resource languages.
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Neural Transduction for Multilingual Lexical Translation (2020.coling-main)

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Challenge: a method for completing multilingual translation dictionaries is proposed . a 27% relative improvement in whole-word accuracy is achieved when multilingual data is unavailable .
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Challenge: Multi-word expressions (MWEs) are a challenging task in natural language processing . they are defined as combinations of at least two words with distinct lexical, morphological, syntactic, semantic or statistical characteristics.
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Challenge: State-of-the-art multilingual models depend on vocabularies that cover all languages . but the methods for generating those vocalaries are not ideal for massively multilingual applications.
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Challenge: Sememe knowledge bases (SKBs) are used to analyze natural language processing.
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Challenge: a lexical resource associates words with concepts in multiple languages, which makes it difficult to combine information from multiple resources.
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