Leveraging Natural Supervision: Learning Semantic Knowledge from Wikipedia

Author: Mingda Chen. Table of Links Abstract Acknowledgements 1 INTRODUCTION 1.1 Overview 1.2 Contributions 2 BACKGROUND 2.1 Self-Supervised Language Pretraining 2.2 Naturally-Occurring Data Structures 2.3 Sentence Variational Autoencoder 2.4 Summary 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.2 Improving In-Context Few-Shot Learning via Self-Supervised Training 3.

4 Summary 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.

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Leveraging Natural Supervision for Language Representation Learning and Generation: AbstractIn this study, researchers describe three lines of work that seek to improve the training and evaluation of neural models using naturally-occurring supervision.
Source: hackernoon - 🏆 532. / 51 Read more »

Leveraging Natural Supervision for Language Representation Learning and Generation: AcknowledgementsIn this study, researchers describe three lines of work that seek to improve the training and evaluation of neural models using naturally-occurring supervision.
Source: hackernoon - 🏆 532. / 51 Read more »