@inproceedings{tjitrahardja-hanif-2025-two,
title = "Two Outliers at {BEA} 2025 Shared Task: Tutor Identity Classification using {D}i{R}e{C}, a Two-Stage Disentangled Contrastive Representation",
author = "Tjitrahardja, Eduardus and
Hanif, Ikhlasul",
editor = {Kochmar, Ekaterina and
Alhafni, Bashar and
Bexte, Marie and
Burstein, Jill and
Horbach, Andrea and
Laarmann-Quante, Ronja and
Tack, Ana{\"i}s and
Yaneva, Victoria and
Yuan, Zheng},
booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bea-1.97/",
pages = "1212--1223",
ISBN = "979-8-89176-270-1",
abstract = "This paper presents DiReC (Disentangled Contrastive Representation), a novel two-stage framework designed to address the BEA 2025 Shared Task 5: Tutor Identity Classification. The task involves distinguishing between responses generated by nine different tutors, including both human educators and large language models (LLMs). DiReC leverages a disentangled representation learning approach, separating semantic content and stylistic features to improve tutor identification accuracy. In Stage 1, the model learns discriminative content representations using cross-entropy loss. In Stage 2, it applies supervised contrastive learning on style embeddings and introduces a disentanglement loss to enforce orthogonality between style and content spaces. Evaluated on the validation set, DiReC achieves strong performance, with a macro-F1 score of 0.9101 when combined with a CatBoost classifier and refined using the Hungarian algorithm. The system ranks third overall in the shared task with a macro-F1 score of 0.9172, demonstrating the effectiveness of disentangled representation learning for tutor identity classification."
}
Markdown (Informal)
[Two Outliers at BEA 2025 Shared Task: Tutor Identity Classification using DiReC, a Two-Stage Disentangled Contrastive Representation](https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bea-1.97/) (Tjitrahardja & Hanif, BEA 2025)
ACL