Improving Language Representation Learning via Sentence Ordering Prediction

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.

architecture. configurations that still have fewer parameters than architecture is similar to , we use a sentence ordering prediction loss, which avoids topic prediction and instead focuses on modeling inter-sentence coherence. The SOP constructs positive examples the same as achieves state-of-the-art results under two settings: single-model and ensembles. When ensembling models, for GLUE benchmark and RACE, we average the model predictions for the ensemble models, where the candidates are fine-tuned from different training steps using the 12-layer and 24-layer architectures. For SQuAD, we average the prediction scores for those spans that have multiple probabilities; we also average the scores of the “unanswerable” decision.

architecture that has significantly fewer parameters than a traditional paper. As a result of these design choices, we are able to scale up to much larger uses a loss based on predicting whether the second segment in a pair has been swapped with a segment from another document. We compare to this loss in our experiments and find that sentence ordering is a more challenging pretraining task and more useful for certain downstream tasks. Concurrently to this work, Wang et al.

is trained jointly by MLM and NSP. NSP is a binary classification task for predicting whether two segments appear consecutively in the original text. Positive examples are created by taking consecutive segments from the training corpus. Negative examples are created by pairing segments from different documents. Positive and negative examples are sampled with equal probability. Later studies show that NSP has little impact on improving downstream task performance. Sentence Ordering Prediction.

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