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Found 7 papers, 0 papers with code
On the Hidden Negative Transfer in Sequential Transfer Learning for Domain Adaptation from News to Tweets
Transfer Learning has been shown to be a powerful tool for Natural Language Processing (NLP) and has outperformed the standard supervised learning paradigm, as it takes benefit from the pre-learned knowledge.
Neural Supervised Domain Adaptation by Augmenting Pre-trained Models with Random Units
In the standard fine-tuning scheme of TL, a model is initially pre-trained on a source domain and subsequently fine-tuned on a target domain and, therefore, source and target domains are trained using the same architecture.
Multi-Task Supervised Pretraining for Neural Domain Adaptation
Two prevalent transfer learning approaches are used in recent works to improve neural networks performance for domains with small amounts of annotated data: Multi-task learning which involves training the task of interest with related auxiliary tasks to exploit their underlying similarities, and Mono-task fine-tuning, where the weights of the model are initialized with the pretrained weights of a large-scale labeled source domain and then fine-tuned with labeled data of the target domain (domain of interest).
Exploration de l'apprentissage par transfert pour l'analyse de textes des r\'eseaux sociaux (Exploring neural transfer learning for social media text analysis )
L{'}apprentissage par transfert repr{\'e}sente la capacit{\'e} qu{'}un mod{\`e}le neuronal entra{\^\i}n{\'e} sur une t{\^a}che {\`a} g{\'e}n{\'e}raliser suffisamment et correctement pour produire des r{\'e}sultats pertinents sur une autre t{\^a}che proche mais diff{\'e}rente.
Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging
Fine-tuning neural networks is widely used to transfer valuable knowledge from high-resource to low-resource domains.
Ranked #1 on
Part-Of-Speech Tagging
on Social media
Using Neural Transfer Learning for Morpho-syntactic Tagging of South-Slavic Languages Tweets
In this paper, we describe a morpho-syntactic tagger of tweets, an important component of the CEA List DeepLIMA tool which is a multilingual text analysis platform based on deep learning.
