Automated Cognate Detection as a Supervised Link Prediction Task with Cognate Transformer (2024.eacl-long)
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| Challenge: | Existing methods for cognate identification are based on distributions of phonemes and make little use of cognacy labels. |
| Approach: | They propose a transformer-based architecture inspired by computational biology for automated cognate detection. |
| Outcome: | The proposed architecture performs better than existing methods with increased supervision. |
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| Challenge: | Phonological reconstruction is one of the central problems in historical linguistics where a proto-word of an ancestral language is determined from the observed cognate words of daughter languages. |
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| Challenge: | Phylogenetic trees are hypotheses of how sets of related languages evolved in time. |
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Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages (2020.coling-main)
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Liviu Dinu, Ana Uban, Alina Cristea, Anca Dinu, Ioan-Bogdan Iordache, Simona Georgescu, Laurentiu Zoicas
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Cognition-aware Cognate Detection (2021.eacl-main)
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Diptesh Kanojia, Prashant Sharma, Sayali Ghodekar, Pushpak Bhattacharyya, Gholamreza Haffari, Malhar Kulkarni
| Challenge: | Existing approaches to cognate detection use orthographic, phonetic and semantic similarity based features sets. |
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| Challenge: | linguistic interpretations of cognate prediction have been based on external analysis (accuracy, raw results, errors). |
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| Challenge: | Existing cognate databases have limited practical applications for research, despite their wide coverage and limited use in lexical tasks. |
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Identifying Cognates in English-Dutch and French-Dutch by means of Orthographic Information and Cross-lingual Word Embeddings (2020.lrec-1)
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| Challenge: | Existing methods to identify cognate pairs in English-Dutch and French-Dutsch combine orthographic information with cross-lingual word embeddings. |
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Can Cognate Prediction Be Modelled as a Low-Resource Machine Translation Task? (2021.findings-acl)
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| Challenge: | Existing work on cognate prediction based on similarities of two languages has not studied their differences or optimized architectural choices. |
| Approach: | They compare statistical and neural MT architectures to a bilingual setup to test their hypothesis . they use monolingual pretraining, backtranslation and multilinguality to test the hypothesis based on the results . |
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