Roxana Petcu — accepted AI-conference papers

2025

A Comprehensive Taxonomy of Negation for NLP and Neural Retrievers

Roxana Petcu, Samarth Bhargav, Maarten de Rijke, Evangelos Kanoulas

EMNLP 2025

Understanding and solving complex reasoning tasks is vital for addressing the information needs of a user. Although dense neural models learn contextualised embeddings, they underperform on queries containing negation. To understand this phenomenon, we study negation in traditional neural informatio

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2025

SOLID: Self-seeding and Multi-intent Self-instructing LLMs for Generating Intent-aware Information-Seeking Dialogs

Arian Askari, Roxana Petcu, Chuan Meng, Mohammad Aliannejadi, Amin Abolghasemi, Evangelos Kanoulas +1

NAACL 2025findings

Intent prediction in information-seeking dialogs is challenging and requires a substantial amount of data with human-labeled intents for effective model training. While Large Language Models (LLMs) have demonstrated effectiveness in generating synthetic data, existing methods typically rely on human…

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