Papers by Taraka Rama
Towards identifying the optimal datasize for lexically-based Bayesian inference of linguistic phylogenies (C18-1)
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| Challenge: | Phylogenetic methods are used for linguistic phylogenies based on cognate matrices for words referring to a fix set of meanings. |
| Approach: | They propose to compute the quartet distance between the most stable meaning and the most unstable meaning . they rank meanings by stability and then compute the optimal number of meanings . |
| Outcome: | The proposed method is based on a set of language families with a fixed set of meanings. |
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 . |
| Outcome: | The proposed methods are fast and easy to use and close to gold standard cognate judgments and expert language family trees. |
Are Automatic Methods for Cognate Detection Good Enough for Phylogenetic Reconstruction in Historical Linguistics? (N18-2)
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| Challenge: | Phylogenetic trees are hypotheses of how sets of related languages evolved in time. |
| Approach: | They compare the performance of automatic cognate detection algorithms to classical manually annotated cognate sets. |
| Outcome: | The proposed methods perform better than classically annotated cognate sets . future work on phylogenetic reconstruction can profit from the results . |
Probing Multilingual BERT for Genetic and Typological Signals (2020.coling-main)
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| Challenge: | Recent cross-lingual models provide representations for about 100 languages and vary in their training objectives. |
| Approach: | They probe the layers in multilingual BERT for phylogenetic and geographic language signals across 100 languages and compute language distances based on the mBERT representations. |
| Outcome: | The proposed model is best explained by phylogenetic and worst by structural factors and correlates with published ranked lists based on linguistic approaches. |