Text Classification with Guidelines Only

Mon, 02 Mar 2026 00:00:00 +0000

Description:
The standard approach for training a machine learning model on a task is to provide an annotated dataset $(\mathcal{X}, \mathcal{Y})$. The dataset is built by providing unlabeled data $\mathcal{X}$ to a group of annotators previously trained on a set of annotation guidelines $\mathcal{G}$. Annotators label data $\mathcal{X}$ via a given class set $\mathcal{C}$. The main issue of this approach is that annotators define the mapping from data $\mathcal{X}$ to the class set $\mathcal{C}$ via the guidelines $\mathcal{G}$, while machine learning models are trained to learn the same mapping without guidelines $\mathcal{G}$. Consequently, these models can learn any kind of mapping from $\mathcal{X}$ to $\mathcal{C}$ that better fits given data. Our idea is to directly provide guidelines $\mathcal{G}$ to models without any access to class labels during training.

Contact: Federico Ruggeri

References:

Let Guidelines Guide You: A Prescriptive Guideline-Centered Data Annotation Methodology
Federico Ruggeri, Eleonora Misino, Arianna Muti, Katerina Korre, Paolo Torroni, Alberto Barrón-Cedeño
September 2024
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Text Classification | Language Technologies Lab

Text Classification with Guidelines Only