Irina Nikishina

irina.a.nikishina@gmail.com

I am a postdoctoral researcher at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) in Abu Dhabi, hosted by Alham Fikri Aji. I joined MBZUAI in September 2026.

My research interests include taxonomies and lexical semantics, question answering, argument mining, retrieval-augmented generation, and NLP for low-resource languages.

Previously, I was a postdoctoral researcher in the Language Technology Group at Universität Hamburg (August 2022–September 2025), hosted by Chris Biemann, working on comparative question answering in the ACQuA-2.0 project. From September 2025 to September 2026, I continued as an independent researcher, collaborating with MBZUAI and supervising students at Hamburg and HSE.

I received my PhD in Computational and Data Science and Engineering from Skoltech in 2022, supervised by Alexander Panchenko. My thesis, Learning Linguistic Tree Structures with Text and Graph Methods, focused on taxonomy enrichment.

Google Scholar · Publications · CV

news

Sep 17, 2026	Our paper “Do I look like a cat.n.01 to you? A Taxonomy Image Generation Benchmark” was included in KONVENS 2026 in Hamburg.
Sep 1, 2026	I joined MBZUAI as a postdoctoral researcher, hosted by Alham Fikri Aji.
Jul 1, 2026	New publications: Multilingual Idioms in Sentences and Conversations Across High-, Medium-, and Low-Resource Languages at ACL 2026, and Argument-Based Comparative Question Answering Evaluation Benchmark at ArgMining 2026.
Oct 6, 2024	Our paper “Low-Resource Machine Translation through the Lens of Personalized Federated Learning” has been accepted to EMNLP 2024 findings! 🎉
Jun 16, 2024	Have a look on our preprint on MT for Lower-Resource languages: arxiv! It was a fruitful collaboration with ML lab in MBZUAI

selected publications

Large Language Models for Creation, Enrichment and Evaluation of Taxonomic Graphs

Viktor Moskvoretskii, Irina Nikishina, Ekaterina Neminova, and 3 more authors

Semantic Web, 2026

Bib HTML

@article{moskvoretskii2026taxonomic,
  title = {Large Language Models for Creation, Enrichment and Evaluation of Taxonomic Graphs},
  author = {Moskvoretskii, Viktor and Nikishina, Irina and Neminova, Ekaterina and Lobanova, Alina and Panchenko, Alexander and Biemann, Chris},
  year = {2026},
  journal = {Semantic Web},
  url = {https://doi.org/10.1177/22104968251404186},
  pages = {1--30},
  volume = {17},
  number = {1},
  doi = {10.1177/22104968251404186}
}

Multilingual Idioms in Sentences and Conversations Across High-, Medium-, and Low-Resource Languages

Saeed Almheiri, Bilal Elbouardi, Salsabila Zahirah Pranida, and 16 more authors

In Proceedings of ACL 2026 (Volume 1: Long Papers), 2026

Bib HTML

@inproceedings{almheiri2026idioms,
  title = {Multilingual Idioms in Sentences and Conversations Across High-, Medium-, and Low-Resource Languages},
  author = {Almheiri, Saeed and Elbouardi, Bilal and Pranida, Salsabila Zahirah and Nikishina, Irina and B, Ashwath Rao and Krishnamurthy, Parameswari and Airlangga, Muhammad Cendekia and Genadi, Rifo Ahmad and Bao, Nguyen Phan Gia and Yari, Amir Hossein and Toyin, Hawau Olamide and Mukhituly, Nurdaulet and Attia, Mena and Hassan, Besher and Hidayatullah, Ahmad Fathan and Kuribayashi, Tatsuki and Li, Haonan and Bhat, Suma and Koto, Fajri},
  year = {2026},
  booktitle = {Proceedings of ACL 2026 (Volume 1: Long Papers)},
  url = {https://aclanthology.org/2026.acl-long.564/},
  pages = {12363--12389}
}

How to Compare Things Properly? A Study of Argument Relevance in Comparative Question Answering

Irina Nikishina, Saba Anwar, Nikolay Dolgov, and 4 more authors

In Proceedings of ACL 2025 (Volume 1: Long Papers), 2025

Bib HTML

@inproceedings{nikishina2025compare,
  title = {How to Compare Things Properly? A Study of Argument Relevance in Comparative Question Answering},
  author = {Nikishina, Irina and Anwar, Saba and Dolgov, Nikolay and Manina, Maria and Ignatenko, Daria and Shelmanov, Artem and Biemann, Chris},
  year = {2025},
  booktitle = {Proceedings of ACL 2025 (Volume 1: Long Papers)},
  url = {https://aclanthology.org/2025.acl-long.765/},
  pages = {15702--15720}
}

Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home

Viktor Moskvoretskii, Maria Marina, Mikhail Salnikov, and 7 more authors

In ACL 2025, 2025

Bib HTML

@inproceedings{moskvoretskii2025adaptive,
  title = {Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home},
  author = {Moskvoretskii, Viktor and Marina, Maria and Salnikov, Mikhail and Ivanov, Nikolay and Pletenev, Sergey and Galimzianova, Daria and Krayko, Nikita and Konovalov, Vasily and Nikishina, Irina and Panchenko, Alexander},
  year = {2025},
  booktitle = {ACL 2025},
}

TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks

Viktor Moskvoretskii, Ekaterina Neminova, Alina Lobanova, and 2 more authors

In Proceedings of the 62nd Conference of the Association for Computational Linguistics, Aug 2024

Bib

@inproceedings{moskvoretskii-etal-2024-taxollama,
  title = {TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks},
  author = {Moskvoretskii, Viktor and Neminova, Ekaterina and Lobanova, Alina and Panchenko, Alexander and Nikishina, Irina},
  booktitle = {Proceedings of the 62nd Conference of the Association for Computational Linguistics},
  month = aug,
  year = {2024},
  address = {Bangkok, Tailand},
  publisher = {Association for Computational Linguistics},
  dimensions = {false},
}

Low-Resource Machine Translation through the Lens of Personalized Federated Learning

Viktor Moskvoretskii, Nazarii Tupitsa, Chris Biemann, and 3 more authors

In Proceedings of the The 2024 Conference on Empirical Methods in Natural Language Processing, Nov 2024

Abs Bib

We present a new approach based on the Personalized Federated Learning algorithm MeritFed that can be applied to Natural Language Tasks with heterogeneous data. We evaluate it on the Low-Resource Machine Translation task, using the dataset from the Large-Scale Multilingual Machine Translation Shared Task (Small Track #2) and the subset of Sami languages from the multilingual benchmark for Finno-Ugric languages. In addition to its effectiveness, MeritFed is also highly interpretable, as it can be applied to track the impact of each language used for training. Our analysis reveals that target dataset size affects weight distribution across auxiliary languages, that unrelated languages do not interfere with the training, and auxiliary optimizer parameters have minimal impact. Our approach is easy to apply with a few lines of code, and we provide scripts for reproducing the experiments at thishttps://github.com/VityaVitalich/MeritFed
@inproceedings{moskvoretskii2024low, title = {Low-Resource Machine Translation through the Lens of Personalized Federated Learning}, author = {Moskvoretskii, Viktor and Tupitsa, Nazarii and Biemann, Chris and Horvath, Samuel and Gorbunov, Eduard and Nikishina, Irina}, journal = {arXiv preprint arXiv:2406.12564}, booktitle = {Proceedings of the The 2024 Conference on Empirical Methods in Natural Language Processing}, month = nov, year = {2024}, address = {Miami, USA}, publisher = {Association for Computational Linguistics}, dimensions = {false}, }
Studying Taxonomy Enrichment on Diachronic WordNet Versions

Irina Nikishina, Varvara Logacheva, Alexander Panchenko, and 1 more author

In Proceedings of the 28th International Conference on Computational Linguistics, Dec 2020

Abs Bib

Ontologies, taxonomies, and thesauri have always been in high demand in a large number of NLP tasks. However, most studies are focused on the creation of lexical resources rather than the maintenance of the existing ones and keeping them up-to-date. In this paper, we address the problem of taxonomy enrichment. Namely, we explore the possibilities of taxonomy extension in a resource-poor setting and present several methods which are applicable to a large number of languages. We also create novel English and Russian datasets for training and evaluating taxonomy enrichment systems and describe a technique of creating such datasets for other languages.
@inproceedings{nikishina-etal-2020-studying, title = {Studying Taxonomy Enrichment on Diachronic {W}ord{N}et Versions}, author = {Nikishina, Irina and Logacheva, Varvara and Panchenko, Alexander and Loukachevitch, Natalia}, editor = {Scott, Donia and Bel, Nuria and Zong, Chengqing}, booktitle = {Proceedings of the 28th International Conference on Computational Linguistics}, month = dec, year = {2020}, address = {Barcelona, Spain (Online)}, publisher = {International Committee on Computational Linguistics}, url = {https://aclanthology.org/2020.coling-main.276}, pages = {3095--3106}, }