@inproceedings{fernandez-pichel-losada-2026-bdi,
title = "{BDI}-batch: Leveraging Standardized Clinical Questionnaires for Contrastive Learning in Psychological Marker Retrieval",
author = "Fern{\'a}ndez-Pichel, Marcos and
Losada, David E.",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1146/",
pages = "24280--24293",
ISBN = "979-8-89176-462-0",
abstract = "Automatic detection of signs of depression has received increasing attention in recent years, evolving from user-level classification (binary) to fine-grained symptom detection. Despite recent advances, state-of-the-art sentence retrieval models still struggle to discriminate between sentences associated with closely related symptoms. In this work, we propose BDI-batch, a novel contrastive learning method that leverages clinically grounded sentence pairs to encode fine-grained distinctions among depression-related symptoms. The method exploits a standardized clinical questionnaire and does not rely on annotated data. We demonstrate the effectiveness of our approach on two early risk detection datasets and under multiple retrieval strategies. Additionally, we explore the use of LLMs to generate synthetic contrastive samples and observe that their performance falls short of questionnaire-derived pairs. Finally, we conduct an error analysis that reveals the most common errors of the sentence retrievers."
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<abstract>Automatic detection of signs of depression has received increasing attention in recent years, evolving from user-level classification (binary) to fine-grained symptom detection. Despite recent advances, state-of-the-art sentence retrieval models still struggle to discriminate between sentences associated with closely related symptoms. In this work, we propose BDI-batch, a novel contrastive learning method that leverages clinically grounded sentence pairs to encode fine-grained distinctions among depression-related symptoms. The method exploits a standardized clinical questionnaire and does not rely on annotated data. We demonstrate the effectiveness of our approach on two early risk detection datasets and under multiple retrieval strategies. Additionally, we explore the use of LLMs to generate synthetic contrastive samples and observe that their performance falls short of questionnaire-derived pairs. Finally, we conduct an error analysis that reveals the most common errors of the sentence retrievers.</abstract>
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%0 Conference Proceedings
%T BDI-batch: Leveraging Standardized Clinical Questionnaires for Contrastive Learning in Psychological Marker Retrieval
%A Fernández-Pichel, Marcos
%A Losada, David E.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F fernandez-pichel-losada-2026-bdi
%X Automatic detection of signs of depression has received increasing attention in recent years, evolving from user-level classification (binary) to fine-grained symptom detection. Despite recent advances, state-of-the-art sentence retrieval models still struggle to discriminate between sentences associated with closely related symptoms. In this work, we propose BDI-batch, a novel contrastive learning method that leverages clinically grounded sentence pairs to encode fine-grained distinctions among depression-related symptoms. The method exploits a standardized clinical questionnaire and does not rely on annotated data. We demonstrate the effectiveness of our approach on two early risk detection datasets and under multiple retrieval strategies. Additionally, we explore the use of LLMs to generate synthetic contrastive samples and observe that their performance falls short of questionnaire-derived pairs. Finally, we conduct an error analysis that reveals the most common errors of the sentence retrievers.
%U https://aclanthology.org/2026.findings-emnlp.1146/
%P 24280-24293
Markdown (Informal)
[BDI-batch: Leveraging Standardized Clinical Questionnaires for Contrastive Learning in Psychological Marker Retrieval](https://aclanthology.org/2026.findings-emnlp.1146/) (Fernández-Pichel & Losada, Findings 2026)
ACL