Papers by Marinela Parović
Investigating the Potential of Task Arithmetic for Cross-Lingual Transfer (2024.eacl-short)
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| Challenge: | Massively multilingual Transformer-based models (MMTs) can learn representations which have a degree of cross-lingual alignment despite being trained using purely unsupervised objectives. |
| Approach: | They propose a modular approach to cross-lingual transfer using task arithmetic . they show that modularity can be achieved even with full model fine-tuning . |
| Outcome: | The proposed approach shows strong performance on multilingual benchmarks encompassing both high-resource and low-resourced languages. |
Generating Domain-Specific Knowledge Graphs from Large Language Models (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have shown impressive world knowledge across different benchmarks and domains but their knowledge is inconveniently scattered across their billions of parameters. |
| Approach: | They propose a prompt-based method to extract knowledge solely from LLMs’ parameters to construct domain-specific KGs by a schema-based process. |
| Outcome: | The proposed method generates large domain-specific KGs containing tens of thousands of entities and relations, and then evaluates against Wikidata, an open-source human-created KG. |
BAD-X: Bilingual Adapters Improve Zero-Shot Cross-Lingual Transfer (2022.naacl-main)
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| Challenge: | Massively multilingual Transformers (MMTs) have dominated research in multilingual NLP and cross-lingual transfer recently. |
| Approach: | They propose to learn bilingual language pair adapters (BAs) when the goal is to optimize performance for a particular source-target transfer direction. |
| Outcome: | The proposed framework improves performance in three standard downstream tasks and for the majority of low-resource languages. |
Unifying Cross-Lingual Transfer across Scenarios of Resource Scarcity (2023.emnlp-main)
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| Challenge: | Existing approaches to deal with resource scarcity have not been developed to deal effectively with the problem. |
| Approach: | They propose to use a set of tools to harness data from one or more high-resource "source" languages to compensate for a shortage of data in low-resourced "target" languages. |
| Outcome: | The proposed technique can be easily adapted to unseen languages, extending the range of the proposed technique and translation-based transfer more broadly. |