5 papers · 1 filter
Rethinking the Role of Text Complexity in Language Model Pretraining
Dan John Velasco, Matthew Theodore Roque
Improving pretraining data quality and size is known to boost downstream performance, but the role of text complexity--how hard a text is to read--remains less explored. We reduce…
Beyond Repetition: Text Simplification and Curriculum Learning for Data-Constrained Pretraining
Matthew Theodore Roque, Dan John Velasco
Most studies on language model pretraining focus on large datasets, leaving open questions about optimization in data-constrained settings. In such settings, the effects of trainin…
Scaling, Simplification, and Adaptation: Lessons from Pretraining on Machine-Translated Text
Dan John Velasco, Matthew Theodore Roque
Most languages lack sufficient data for large-scale monolingual pretraining, creating a "data wall." Multilingual pretraining helps but is limited by language imbalance and the "cu…
Pagsusuri ng RNN-based Transfer Learning Technique sa Low-Resource Language
Dan John Velasco
Low-resource languages such as Filipino suffer from data scarcity which makes it challenging to develop NLP applications for Filipino language. The use of Transfer Learning (TL) te…
Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets
Jan Christian Blaise Cruz, Jose Kristian Resabal, James Lin +2
Transformers represent the state-of-the-art in Natural Language Processing (NLP) in recent years, proving effective even in tasks done in low-resource languages. While pretrained t…