We analyze the masked language modeling pretraining objective function from the perspective of the Distributional Hypothesis.
We investigate whether the better sample efficiency and
the better generalization capability of models
pretrained with masked language modeling can
be attributed to the semantic similarity encoded in the pretraining data's distributional property.
Via a synthetic dataset, our analysis suggests that distributional property indeed leads to the better sample efficiency of pretrained masked language models, but 
does not fully explain the generalization capability.
We also conduct an analysis over two real-world datasets 
and demonstrate that the distributional property does not explain the generalization ability
of pretrained natural language models either.
Our results illustrate our limited understanding of model pretraining and provide future research directions.

The Distributional Hypothesis Does Not Fully Explain the Benefits of Masked Language Model Pretraining | VIDEO

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**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

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Dani Yogatama

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Presentations

The Distributional Hypothesis Does Not Fully Explain the Benefits of Masked Language Model Pretraining | VIDEO