@inproceedings{paul-etal-2026-toni,
title = "{TONI}-{NLP} at {P}sy{D}ef{D}etect: Defense Mechanism Detection via {LLM}-based Ensemble Methods",
author = "Paul, Durjoy and
Basavaraj, Arshitha and
Chan, Callum and
Perez-Rosas, Veronica and
Inkpen, Diana and
Pereira, Francisco and
Lossio-Ventura, Juan Antonio",
editor = "Gupta, Deepak and
Demner-Fushman, Dina",
booktitle = "Proceedings of the {B}io{NLP} 2026 (Shared Tasks)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.bionlp-2.19/",
pages = "132--140",
ISBN = "979-8-89176-435-4",
abstract = "This system paper presents the approach of Team TONI-NLP to the PsyDefDetect 2026 shared task. The objective of the task was to classify utterances from helper{--}seeker conversations into nine categories: seven labels representing progressively higher levels of defensive maturity, one label indicating the absence of a defense mechanism, and one label for cases requiring additional information. We investigated several modern NLP approaches, including prompt engineering, fine-tuning, hierarchical modeling and classification using text embeddings derived from transformer-based models as well as classical embeddings such as TF-IDF. Our results show that ensemble methods performed best among our submitted systems, achieving a macro-F1 score of 0.320 and ranking 9th in the shared task out of 21 teams."
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<abstract>This system paper presents the approach of Team TONI-NLP to the PsyDefDetect 2026 shared task. The objective of the task was to classify utterances from helper–seeker conversations into nine categories: seven labels representing progressively higher levels of defensive maturity, one label indicating the absence of a defense mechanism, and one label for cases requiring additional information. We investigated several modern NLP approaches, including prompt engineering, fine-tuning, hierarchical modeling and classification using text embeddings derived from transformer-based models as well as classical embeddings such as TF-IDF. Our results show that ensemble methods performed best among our submitted systems, achieving a macro-F1 score of 0.320 and ranking 9th in the shared task out of 21 teams.</abstract>
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%0 Conference Proceedings
%T TONI-NLP at PsyDefDetect: Defense Mechanism Detection via LLM-based Ensemble Methods
%A Paul, Durjoy
%A Basavaraj, Arshitha
%A Chan, Callum
%A Perez-Rosas, Veronica
%A Inkpen, Diana
%A Pereira, Francisco
%A Lossio-Ventura, Juan Antonio
%Y Gupta, Deepak
%Y Demner-Fushman, Dina
%S Proceedings of the BioNLP 2026 (Shared Tasks)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-435-4
%F paul-etal-2026-toni
%X This system paper presents the approach of Team TONI-NLP to the PsyDefDetect 2026 shared task. The objective of the task was to classify utterances from helper–seeker conversations into nine categories: seven labels representing progressively higher levels of defensive maturity, one label indicating the absence of a defense mechanism, and one label for cases requiring additional information. We investigated several modern NLP approaches, including prompt engineering, fine-tuning, hierarchical modeling and classification using text embeddings derived from transformer-based models as well as classical embeddings such as TF-IDF. Our results show that ensemble methods performed best among our submitted systems, achieving a macro-F1 score of 0.320 and ranking 9th in the shared task out of 21 teams.
%U https://aclanthology.org/2026.bionlp-2.19/
%P 132-140
Markdown (Informal)
[TONI-NLP at PsyDefDetect: Defense Mechanism Detection via LLM-based Ensemble Methods](https://aclanthology.org/2026.bionlp-2.19/) (Paul et al., BioNLP 2026)
ACL
- Durjoy Paul, Arshitha Basavaraj, Callum Chan, Veronica Perez-Rosas, Diana Inkpen, Francisco Pereira, and Juan Antonio Lossio-Ventura. 2026. TONI-NLP at PsyDefDetect: Defense Mechanism Detection via LLM-based Ensemble Methods. In Proceedings of the BioNLP 2026 (Shared Tasks), pages 132–140, San Diego, California, USA. Association for Computational Linguistics.