Papers by Anastassia Shaitarova
SpiritRAG: A Q&A System for Religion and Spirituality in the United Nations Archive (2025.emnlp-demos)
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Yingqiang Gao, Fabian Winiger, Patrick Montjourides, Anastassia Shaitarova, Nianlong Gu, Simon Peng-Keller, Gerold Schneider
| Challenge: | Religion and spirituality (R/S) are complex and domain-dependent concepts that have long confounded researchers and policymakers. |
| Approach: | They propose an interactive question-answering system based on Retrieval-Augmented Generation (RAG) SpiritRAG allows researchers and policymakers to conduct complex, context-sensitive database searches of large datasets . |
| Outcome: | SpiritRAG is an interactive Q&A system based on Retrieval-Augmented Generation (RAG) built using 7,500 UN resolution documents related to religion and spirituality in the domains of health and education. |
Negation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish. (2021.naacl-srw)
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| Challenge: | Negation resolution remains an acute and continuously researched question in Natural Language Processing. |
| Approach: | They propose to use multilingual pre-trained general representation models to detect negation scope in languages without annotated data. |
| Outcome: | The proposed model achieves token-level F1 score between English, Spanish, French, and Russian. |
ConLoan: A Contrastive Multilingual Dataset for Evaluating Loanwords (2025.acl-long)
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Sina Ahmadi, Micha David Hess, Elena Álvarez-Mellado, Alessia Battisti, Cui Ding, Anne Göhring, Yingqiang Gao, Zifan Jiang, Andrianos Michail, Peshmerge Morad, Joel Niklaus, Maria Christina Panagiotopoulou, Stefano Perrella, Juri Opitz, Anastassia Shaitarova, Rico Sennrich
| Challenge: | Lexical borrowing is a ubiquitous linguistic phenomenon influenced by geopolitical, societal, and technological factors. |
| Approach: | They propose a novel contrastive dataset comprising sentences with and without loanwords across 10 languages to examine how machine translation and language models process loanword . |
| Outcome: | The proposed dataset shows that state-of-the-art models prefer loanwords over native terms and exhibit varying performance across languages. |
Subword Evenness (SuE) as a Predictor of Cross-lingual Transfer to Low-resource Languages (2022.emnlp-main)
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| Challenge: | English is the most natural choice for cross-lingual transfer, but it is often not the best choice for low-resource languages. |
| Approach: | They propose to use pre-trained multilingual models to improve performance in low-resource languages via cross-lingual transfer. |
| Outcome: | The results show that languages written in non-Latin and non-alphabetic scripts are the best choices for improving performance on Masked Language Modelling tasks in a diverse set of 30 low-resource languages. |
Resolving Legalese: A Multilingual Exploration of Negation Scope Resolution in Legal Documents (2024.lrec-main)
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| Challenge: | Negation scope resolution is a challenging task for NLP because of the complexity of legal texts and lack of annotated in-domain negation corpora. |
| Approach: | They propose to use annotated court decisions to improve negation scope resolution . they release annotations in german, french, and italian to train models without legal data . |
| Outcome: | The proposed models achieve token-level F1-scores of up to 86.7% in zero-shot and multilingual settings. |