Papers by Ivan Sekulic
“Stupid robot, I want to speak to a human!” User Frustration Detection in Task-Oriented Dialog Systems (2025.coling-industry)
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Mireia Hernandez Caralt, Ivan Sekulic, Filip Carevic, Nghia Khau, Diana Nicoleta Popa, Bruna Guedes, Victor Guimaraes, Zeyu Yang, Andre Manso, Meghana Reddy, Paolo Rosso, Roland Mathis
| Challenge: | Detecting user frustration in task-oriented dialog systems is imperative for maintaining overall user satisfaction, engagement and retention. |
| Approach: | They compare out-of-the-box methods for user frustration detection with open-source methods . they find an LLM-based approach is promising, as it captures both emotion and dialog breakdowns . |
| Outcome: | The proposed method outperforms open-source methods in detecting user frustration in a TOD system. |
Adapting Deep Learning Methods for Mental Health Prediction on Social Media (D19-55)
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| Challenge: | a quarter of the population in Europe suffers from an episode of a mental disorder in their life, according to the World Health Organization . text analysis of rich resources like social media can contribute to deeper understanding of mental health and provide means for their early detection. |
| Approach: | They propose to use a hierarchical attention network to predict if a user suffers from one of nine disorders to adapt a deep neural model to the task. |
| Outcome: | The proposed model outperforms previous benchmarks for four out of nine disorders in a binary classification task on social media. |
Reasoning with Latent Structure Refinement for Document-Level Relation Extraction (2020.acl-main)
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| Challenge: | Existing methods for document-level relation extraction capture non-local interactions but are not able to capture rich non-linguistic interactions. |
| Approach: | They propose a document-level relation extraction model that empowers relational reasoning across sentences by automatically inducing the latent document- level graph. |
| Outcome: | The proposed model achieves an F1 score of 59.05 on a large-scale document-level dataset (DocRED), significantly improving over the previous results. |
Efficient Out-of-Scope Detection in Dialogue Systems via Uncertainty-Driven LLM Routing (2025.acl-industry)
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| Challenge: | Out-of-scope (OOS) intent detection is critical in task-oriented dialogue systems . without effective OOS detection, such inputs could lead to incorrect responses, reduced user trust, and eventual system failures. |
| Approach: | They propose a modular framework that combines uncertainty modeling with fine-tuned large language models (LLMs) their method yields state-of-the-art results on key OOS detection benchmarks . |
| Outcome: | The proposed framework yields state-of-the-art results on key OOS detection benchmarks including real-world OOS data. |