Papers by Kirill Chirkunov

2 papers
From Multiple-Choice to Extractive QA: A Case Study for English and Arabic (2025.coling-main)

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Challenge: Recent years have brought about very fast developments in Natural Language Processing (NLP), but many other languages are overlooked due to limited resources.
Approach: They propose to repurpose a multilingual BELEBELE dataset for a task of extractive QA in the style of machine reading comprehension.
Outcome: The proposed approach could be used to extract QA in the style of machine reading comprehension.
Linear Semantic Segmentation for Low-Resource Spoken Dialects (2026.findings-acl)

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Challenge: Existing models for semantic segmentation are primarily developed and evaluated on high-resource written text, limiting their effectiveness on low-resourced conversational varieties.
Approach: They propose a multi-genre benchmark for semantic segmentation in Arabic, focusing on dialectal discourse.
Outcome: The proposed model outperforms baselines on dialectal non-news genres while performing well on high-resource written text.

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