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About Multi-Task Deep Neural Networks For Natural Language Understanding
1. This document presents a Multi-Task Deep Neural Network (MT-DNN) that leverages large amounts of cross-task data and benefits from regularization to learn general natural language representations for multiple natural language understanding tasks. 2. MT-DNN extends a previous model by incorporating BERT, using BERT's pretrained bidirectional transformer language model as shared text encoding layers, while adding task-specific layers for different types of tasks like classification, similarity, and ranking. 3. In experiments, MT-DNN achieves state-of-the-art results on ten natural language understanding tasks, including eight out of nine GLUE benchmark tasks, demonstrating the effectiveness of combining multi-task learning with pretrained language models.
Detailed Information
| Author: | ['Sana Khan'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 10 |
| Language: | English |
| Format: | |
| Price: | FREE |
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