Search Results for author:
Found 5 papers, 3 papers with code
Transformers: State-of-the-Art Natural Language Processing
Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks.
HuggingFace's Transformers: State-of-the-art Natural Language Processing
Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks.
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
We introduce a new approach to generative data-driven dialogue systems (e. g. chatbots) called TransferTransfo which is a combination of a Transfer learning based training scheme and a high-capacity Transformer model.
Ranked #3 on
Dialogue Generation
on Persona-Chat
(using extra training data)
Continuous Learning in a Hierarchical Multiscale Neural Network
We reformulate the problem of encoding a multi-scale representation of a sequence in a language model by casting it in a continuous learning framework.
Meta-Learning a Dynamical Language Model
We consider the task of word-level language modeling and study the possibility of combining hidden-states-based short-term representations with medium-term representations encoded in dynamical weights of a language model.
