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Deep reinforcement learning: An overview
Y Li - ar** machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …
relationships has been a longstanding goal of AI. Over the last decade, large-scale …
[HTML][HTML] GPT understands, too
Prompting a pretrained language model with natural language patterns has been proved
effective for natural language understanding (NLU). However, our preliminary study reveals …
effective for natural language understanding (NLU). However, our preliminary study reveals …
Graph neural networks for natural language processing: A survey
Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …
Language Processing (NLP). Although text inputs are typically represented as a sequence …
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 …
better understanding of the human language for linguistic-based human-computer …
An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
For most deep learning practitioners, sequence modeling is synonymous with recurrent
networks. Yet recent results indicate that convolutional architectures can outperform …
networks. Yet recent results indicate that convolutional architectures can outperform …
Universal language model fine-tuning for text classification
Inductive transfer learning has greatly impacted computer vision, but existing approaches in
NLP still require task-specific modifications and training from scratch. We propose Universal …
NLP still require task-specific modifications and training from scratch. We propose Universal …
Simple bert models for relation extraction and semantic role labeling
We present simple BERT-based models for relation extraction and semantic role labeling. In
recent years, state-of-the-art performance has been achieved using neural models by …
recent years, state-of-the-art performance has been achieved using neural models by …
Semantics-aware BERT for language understanding
The latest work on language representations carefully integrates contextualized features into
language model training, which enables a series of success especially in various machine …
language model training, which enables a series of success especially in various machine …
Detecting formal thought disorder by deep contextualized word representations
Computational linguistics has enabled the introduction of objective tools that measure some
of the symptoms of schizophrenia, including the coherence of speech associated with formal …
of the symptoms of schizophrenia, including the coherence of speech associated with formal …