3 papers
cs.CL2021
Neural Supervised Domain Adaptation by Augmenting Pre-trained Models with Random Units
Sara Meftah, Nasredine Semmar, Youssef Tamaazousti +2
Neural Transfer Learning (TL) is becoming ubiquitous in Natural Language Processing (NLP), thanks to its high performance on many tasks, especially in low-resourced scenarios. Nota…
cs.CL2019
Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging
Sara Meftah, Youssef Tamaazousti, Nasredine Semmar +2
Fine-tuning neural networks is widely used to transfer valuable knowledge from high-resource to low-resource domains. In a standard fine-tuning scheme, source and target problems a…
cs.IR2018
Sequence to Sequence Learning for Query Expansion
Salah Zaiem, Fatiha Sadat
Using sequence to sequence algorithms for query expansion has not been explored yet in Information Retrieval literature nor in Question-Answering's. We tried to fill this gap in th…