Papers by Irina Nikishina
Studying Taxonomy Enrichment on Diachronic WordNet Versions (2020.coling-main)
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| Challenge: | Ontologies, taxonomies and thesauri are used in many NLP tasks but are often not maintained. |
| Approach: | They propose methods for taxonomy enrichment in a resource-poor setting . they also create novel datasets for training and evaluating taxonomies . |
| Outcome: | The proposed methods are applicable to English and Russian datasets and can be used in other languages. |
Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home (2025.acl-long)
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Viktor Moskvoretskii, Maria Marina, Mikhail Salnikov, Nikolay Ivanov, Sergey Pletenev, Daria Galimzianova, Nikita Krayko, Vasily Konovalov, Irina Nikishina, Alexander Panchenko
| Challenge: | Recent adaptive retrieval methods integrate LLMs’ intrinsic knowledge with external information appealing to LLM self-knowledge, but they often neglect efficiency evaluations and comparisons with uncertainty estimation techniques. |
| Approach: | They propose to integrate LLMs’ intrinsic knowledge with external information appealing to LLM self-knowledge but neglect efficiency evaluations and comparisons with uncertainty estimation techniques. |
| Outcome: | The proposed methods outperform complex pipelines in terms of efficiency and self-knowledge while maintaining comparable QA performance. |
Are Large Language Models Good at Lexical Semantics? A Case of Taxonomy Learning (2024.lrec-main)
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| Challenge: | Recent studies on LLMs do not pay enough attention to linguistic and lexical semantic tasks, such as taxonomy learning. |
| Approach: | They propose a method for stochastic graph traversal and a new algorithm for data collection . they propose LLaMA-2 and Mistral for a lexical semantic task . |
| Outcome: | The proposed models can perform linguistic and lexical tasks, but they lack basic skills in taxonomy learning. |
TaxFree: a Visualization Tool for Candidate-free Taxonomy Enrichment (2022.aacl-demo)
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| Challenge: | In this paper, we present an open source system for taxonomy visualisation and automatic taxonomies enrichment without pre-defined candidates. |
| Approach: | They propose an open source system for taxonomy visualisation and automatic taxonomie enrichment without pre-defined candidates on the example of WordNet-3.0. |
| Outcome: | The proposed system can be used for visualisation and inspection of taxonomies without pre-defined candidates on WordNet-3.0. |
TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks (2024.acl-long)
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| Challenge: | Recent studies in Natural Language Processing widely utilize Large Language Models (LLMs) for their capability to store extensive knowledge. |
| Approach: | They propose an LLM-based model that captures lexical-semantic knowledge from WordNet and test it on multiple lexicals. |
| Outcome: | The proposed model achieves 11 SOTA results and 4 top-2 results out of 16 taxonomy-related tasks. |
How to Compare Things Properly? A Study of Argument Relevance in Comparative Question Answering (2025.acl-long)
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Irina Nikishina, Saba Anwar, Nikolay Dolgov, Maria Manina, Daria Ignatenko, Artem Shelmanov, Chris Biemann
| Challenge: | Comparative Question Answering (CQA) is a task that involves processing information and diverse viewpoints. |
| Approach: | They construct a dataset of arguments annotated with their relevance and use it to answer comparative questions. |
| Outcome: | The proposed dataset contains arguments annotated with their relevance and enables precise traceability and faithfulness. |
On Improving Repository-Level Code QA for Large Language Models (2024.acl-srw)
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| Challenge: | Commercial AI-assisted programming Chatbots may generate incorrect information when requests go beyond the model training data or require additional knowledge. |
| Approach: | They propose to implement different self-alignment processes and retrieval-augmented generation pipelines to improve the copilot performance. |
| Outcome: | The proposed model improves the copilot performance on repository-level semantics, dependency between files, and meta-information about the repository. |
Multilingual Idioms in Sentences and Conversations Across High-, Medium-, and Low-Resource Languages (2026.acl-long)
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Saeed Almheiri, Bilal Elbouardi, Salsabila Zahirah Pranida, Irina Nikishina, Ashwath Rao B, Parameswari Krishnamurthy, Muhammad Cendekia Airlangga, Rifo Ahmad Genadi, Nguyen Phan Gia Bao, Amir Hossein Yari, Hawau Olamide Toyin, Nurdaulet Mukhituly, Mena Attia, Besher Hassan, Ahmad Fathan Hidayatullah, Tatsuki Kuribayashi, Haonan Li, Suma Bhat, Fajri Koto
| Challenge: | idioms are a major challenge for multilingual NLP because their meanings shift between figurative and literal usage, often requiring context for accurate interpretation. |
| Approach: | They propose a multilingual idiom dataset that provides idiomatic expressions in both sentence-level and conversational contexts. |
| Outcome: | The proposed model performs well with low-resource idioms, but lacks contextual inference. |
Low-Resource Machine Translation through the Lens of Personalized Federated Learning (2024.findings-emnlp)
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Viktor Moskvoretskii, Nazarii Tupitsa, Chris Biemann, Samuel Horváth, Eduard Gorbunov, Irina Nikishina
| Challenge: | Existing approaches to low-resource languages are limited to 500 languages . a lot of tasks for low-rsource languages remain unsolved . |
| Approach: | They propose a new approach called MeritOpt that can be applied to Natural Language Tasks with heterogeneous data. |
| Outcome: | The proposed approach can be applied to a low-resource machine translation task using the datasets of South East Asian and Finno-Ugric languages. |
CAM 2.0: End-to-End Open Domain Comparative Question Answering System (2024.lrec-main)
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Ahmad Shallouf, Hanna Herasimchyk, Mikhail Salnikov, Rudy Alexandro Garrido Veliz, Natia Mestvirishvili, Alexander Panchenko, Chris Biemann, Irina Nikishina
| Challenge: | Comparative Question Answering is a Natural Language Processing task that combines Question Answers and Argument Mining. |
| Approach: | They propose a system for answering comparative questions called CAM 2.0 and a public leaderboard called CompUGE that unifies existing datasets under a single easy-to-use evaluation suite. |
| Outcome: | The proposed system is compared with previous web-form-based systems . it features question identification, object and aspect labeling, stance classification, summarization . the proposed system has a user-friendly interface and is available for free on the web . |
CompUGE-Bench: Comparative Understanding and Generation Evaluation Benchmark for Comparative Question Answering (2025.coling-demos)
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| Challenge: | Comparative Question Answering systems help users make informed decisions by generating comparative information. |
| Approach: | They propose a comprehensive benchmark designed to evaluate Comparative Question Answering systems. |
| Outcome: | The proposed benchmark is available on HuggingFace Spaces . it unifies multiple datasets and provides a robust evaluation platform . |