This paper addresses the challenges in developing language models for less-represented
languages, with a focus on Luxembourgish. Despite its active development, Luxembourgish …

In recent years, pre-trained transformer-based language models (LM) have become a key
resource for implementing most NLP tasks. However, pre-training such models demands …

Orthographic variation is very common in Luxembourgish texts due to the absence of a fully-
fledged standard variety. Additionally, developing NLP tools for Luxembourgish is a difficult …

Large language models are very resource intensive, both financially and environmentally,
and require an amount of training data which is simply unobtainable for the majority of NLP …

The Universal Dependencies (UD) project has significantly expanded linguistic coverage
across 161 languages, yet Luxembourgish, a West Germanic language spoken by …

Natural language processing of Low-Resource Languages (LRL) is often challenged by the
lack of data. Therefore, achieving accurate machine translation (MT) in a low-resource …

Relation extraction is essential for extracting and understanding biographical information in
the context of digital humanities and related subjects. There is a growing interest in the …

Sentence embedding models play a key role in various Natural Language Processing tasks,
such as in Topic Modeling, Document Clustering and Recommendation Systems. However …

Training large language models is challenging when data availability is limited, as it is the
case for low-resource languages. We investigate different data augmentation techniques for …

In NLP, zero-shot classification (ZSC) is the task of assigning labels to textual data without
any labeled examples for the target classes. A common method for ZSC is to fine-tune a …