In this study, researchers describe three lines of work that seek to improve the training and evaluation of neural models using naturally-occurring supervision.
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.
. has 4 candidate answers. Following prior work , we use the concatenation of the passage, question, and each candidate answer as the input to models. Then, we use the representations from the token for predicting the probability of each answer. The dataset consists of two domains: middle school and high school. We train our models on both domains and report accuracies on both the development set and test set. A.
templates in Table A.5. This paper is available on arxiv under CC 4.0 license. Author: Mingda Chen. Author: 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.
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