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Found 4 papers, 4 papers with code
Natural Language Processing Advancements By Deep Learning: A Survey
Natural Language Processing (NLP) helps empower intelligent machines by enhancing a better understanding of the human language for linguistic-based human-computer communication.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Neural Abstractive Text Summarization with Sequence-to-Sequence Models
As part of this survey, we also develop an open source library, namely, Neural Abstractive Text Summarizer (NATS) toolkit, for the abstractive text summarization.
Deep Transfer Reinforcement Learning for Text Summarization
Deep neural networks are data hungry models and thus face difficulties when attempting to train on small text datasets.
Deep Reinforcement Learning For Sequence to Sequence Models
In this survey, we consider seq2seq problems from the RL point of view and provide a formulation combining the power of RL methods in decision-making with sequence-to-sequence models that enable remembering long-term memories.
