Papers by Boshko Koloski
A Computational Analysis of the Dehumanisation of Migrants from Syria and Ukraine in Slovene News Media (2024.lrec-main)
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| Challenge: | Dehumanisation involves the perception and/or treatment of a social group’s members as less than human. |
| Approach: | They propose to use a new sentiment resource to make it easier to transfer to other languages and to evaluate and use . they then apply the method to study attitudes to migration expressed in Slovene newspapers, and examine how this discourse changed between the 2015-16 migration crisis and the 2022-23 period following the war in Ukraine. |
| Outcome: | The proposed method is easier to transfer to other languages and evaluates . it combines zero-shot cross-lingual valence and arousal detection with statistical significance testing to examine attitudes to migration expressed in Slovene newspapers . |
SEKE: Specialised Experts for Keyword Extraction (2025.findings-emnlp)
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| Challenge: | Keyword extraction involves identifying the most descriptive words in a document . supervised keyword extraction is based on the mixture of experts (MoE) technique . |
| Approach: | They propose a supervised keyword extraction approach based on the mixture of experts technique . they use a learnable routing sub-network to direct information to specialised experts . |
| Outcome: | The proposed approach is based on the mixture of experts (MoE) technique . experts attend to each token and integrate it with a bidirectional long-term memory network . |
Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised? (2022.lrec-1)
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| Challenge: | Keyword extraction is the task of retrieving words that are essential to the content of a document. |
| Approach: | They propose to use pretrained multilingual language models for zero-shot cross-lingual keyword extraction on low-resource languages with limited or no available labeled training data. |
| Outcome: | The proposed models outperform state-of-the-art unsupervised methods on low-resource languages with limited or no training data. |