| Challenge: | Low-resource languages often suffer from a lack of high-coverage lexical resources. |
| Approach: | They propose a method to generate cognate tables by clustering words from existing lexical resources. |
| Outcome: | The proposed method outperforms baselines on the Romance and Turkic language families. |
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| Challenge: | Existing cognate databases have limited practical applications for research, despite their wide coverage and limited use in lexical tasks. |
| Approach: | They introduce a new large-scale lexical database that provides cognates across languages. |
| Outcome: | The proposed database contains 3.1 million cognate pairs across 338 languages and has an accuracy of 94%. |
Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages (2020.coling-main)
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Diptesh Kanojia, Raj Dabre, Shubham Dewangan, Pushpak Bhattacharyya, Gholamreza Haffari, Malhar Kulkarni
| Challenge: | a study of 14 Indian languages shows that cognates can be detected by word embeddings . cognates are variants of the same lexical form across languages . |
| Approach: | They propose to use cross-lingual word embeddings to detect cognates among 14 Indian languages . they then evaluate the impact of their method on neural machine translation . |
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RoBoCoP: A Comprehensive ROmance BOrrowing COgnate Package and Benchmark for Multilingual Cognate Identification (2023.emnlp-main)
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Liviu Dinu, Ana Uban, Alina Cristea, Anca Dinu, Ioan-Bogdan Iordache, Simona Georgescu, Laurentiu Zoicas
| Challenge: | Existing databases for romance cognates are scattered, incomplete, noisy, or have uncertain availability. |
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Massively Translingual Compound Analysis and Translation Discovery (L18-1)
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| Challenge: | Morphological compounding is one of the most common and productive methods of word formation across the world's languages. |
| Approach: | They propose a model for compounding using bilingual dictionaries and no annotated training data . they also release a massively multilingual dataset of compound words and their decompositions . |
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Automatic Reconstruction of Missing Romanian Cognates and Unattested Latin Words (2020.lrec-1)
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| Challenge: | Existing methods for producing related words are based on sequence labeling . |
| Approach: | They propose a method for producing related words based on sequence labeling . they aim to fill in gaps in incomplete cognate sets in Romance languages with Latin etymology and reconstruct uncertified Latin words. |
| Outcome: | The proposed method fills in gaps in incomplete cognate sets in Romance languages with Latin etymology and reconstructs uncertified Latin words. |
Improving Multilingual Models with Language-Clustered Vocabularies (2020.emnlp-main)
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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. |
| Approach: | They propose a procedure for multilingual vocabulary generation that combines separately trained vocabularies of several automatically derived language clusters. |
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Automatically Building a Multilingual Lexicon of False Friends With No Supervision (2020.lrec-1)
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| Challenge: | a method to detect false friends from cognates is developed . cognates are words in genetically related languages with a common proto-word . in some cases, cognates have diverged from the common etymon and their meanings became different from each other. |
| Approach: | They propose an automatic method to detect false friends from a set of cognates . cognates are words in genetically related languages which derive from etymons . authors propose a measure of "falseness" of a false friends pair based on cross-lingual word embeddings based in the system . |
| Outcome: | The proposed method can be extended to any language pair, with monolingual corpora and a bilingual dictionary. |
An Automated Framework for Fast Cognate Detection and Bayesian Phylogenetic Inference in Computational Historical Linguistics (P19-1)
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| Challenge: | Existing methods for phylogenetic reconstruction of large datasets require time and computational power. |
| Approach: | They propose a workflow for phylogenetic reconstruction on large datasets using two methods . they use a method for fast detection of cognates and a Bayesian method for inference . their results show that the methods take less than a few minutes to process language families . |
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Multilingual Dependency Parsing for Low-Resource Languages: Case Studies on North Saami and Komi-Zyrian (L18-1)
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| Challenge: | Developing systems for low-resource languages is a crucial issue for Natural Language Processing (NLP). |
| Approach: | They propose a method for parsing low-resource languages with very small training corpora using multilingual word embeddings and annotated corporata of larger languages. |
| Outcome: | The proposed method improves dependency parsing for low-resource languages with very small training corpora compared to previous work . it also explores whether contemporary contact languages or genetically related languages would be the most fruitful starting point for multilingual parsers. |
Romanization-based Large-scale Adaptation of Multilingual Language Models (2023.findings-emnlp)
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| Challenge: | Large multilingual pretrained language models are limited by their vocabulary size and parameter budget. |
| Approach: | They explore the potential of leveraging transliteration on a massive scale to improve performance for multilingual pretrained language models. |
| Outcome: | The proposed transliteration tool outperforms other methods on low-resource languages. |