The field of Natural Language Generation (NLG) suffers from a severe shortage of labeled
data due to the extremely expensive and time-consuming process involved in manual …

Text classification datasets from specialised or technical domains are in high demand,
especially in industrial applications. However, due to the high cost of annotation such …

The Transformer Language Model is a powerful tool that has been shown to excel at various
NLP tasks and has become the de-facto standard solution thanks to its versatility. In this …

One solution to limited annotation budgets is active learning (AL), a collaborative process of
human and machine to strategically select a small but informative set of examples. While …

Data labeling for multi-label text is a challenging task in natural language processing, and
active learning has emerged as a promising approach to reduce annotation effort while …

Text classification (TC) is a foundational technique in natural language processing (NLP).
The ability to automatically classify texts into predetermined categories serves a critical role …

Engaging citizens in decision-making processes is a widely implemented instrument in
democracies. Such public participation processes serve the goal of achieving a more …

Deep active learning (DAL) studies the optimal selection of labeled data for training deep
neural networks (DNNs). While data selection in traditional active learning is mostly …

L'annotation des données est cruciale pour l'apprentissage automatique, notamment dans
les domaines techniques, où la qualité et la quantité des données annotées affectent …

This paper describes our proposed models in the BabyLM Challenge (Warstadt et al., 2023).
The goal of this shared task is to pretrain models efficiently using a developmentally …