A Mutual Information Maximization Perspective of Language Representation Learning (1910.08350v2)

Abstract: We show state-of-the-art word representation learning methods maximize an objective function that is a lower bound on the mutual information between different parts of a word sequence (i.e., a sentence). Our formulation provides an alternative perspective that unifies classical word embedding models (e.g., Skip-gram) and modern contextual embeddings (e.g., BERT, XLNet). In addition to enhancing our theoretical understanding of these methods, our derivation leads to a principled framework that can be used to construct new self-supervised tasks. We provide an example by drawing inspirations from related methods based on mutual information maximization that have been successful in computer vision, and introduce a simple self-supervised objective that maximizes the mutual information between a global sentence representation and n-grams in the sentence. Our analysis offers a holistic view of representation learning methods to transfer knowledge and translate progress across multiple domains (e.g., natural language processing, computer vision, audio processing).

• The paper proposes a theoretical framework reinterpreting popular language representation learning methods, like Skip-gram and BERT, as optimizing mutual information.
• It derives that state-of-the-art techniques inherently maximize the InfoNCE lower bound on mutual information, offering a unified view of static versus contextual models.
• The analysis introduces a new self-supervised task maximizing mutual information between global sentence representations and n-grams, demonstrating strong performance in tasks like question answering.

A Mutual Information Maximization Perspective of Language Representation Learning

The paper "A Mutual Information Maximization Perspective of Language Representation Learning" offers a compelling reinterpretation of the prevalent methods used for word representation learning, presenting them as optimizing mutual information between parts of a sentence. This interpretation frames popular techniques such as Skip-gram and contemporary methods including BERT and XLNet within a theoretical structure inspired by InfoMax principles.

Summary of Key Contributions

The authors propose a comprehensive theoretical framework where state-of-the-art word representation methods are united under the objective of maximizing a lower bound on mutual information. This perspective affords deeper understanding and offers avenues to design novel self-supervised tasks for NLP. The mutual information perspective aligns closely with InfoMax principles, particularly as practiced in fields such as computer vision and reinforcement learning, while developing connections to contrastive learning approaches utilized in other domains.

Numerical and Theoretical Insights

Through rigorous analysis, the authors establish that classical methods like Skip-gram and modern contextual representation models such as BERT and XLNet inherently perform mutual information maximization. They effectively derive that these techniques aim to maximize the InfoNCE lower bound on mutual information. This realization allows for a unified view where the major difference lies in whether models learn contextual or static representations.

The paper also introduces a new self-supervised task under this framework, leveraging inspirations from contrastive methods shown to be effective beyond NLP. The presented objective maximizes the mutual information between global sentence representations and $n$-grams within the same sentence. Experimental evaluations show that this composite objective performs exceptionally, particularly in tasks that involve a deeper understanding of the linguistic structure such as question answering.

Implications and Future Work

The analysis presented in the paper has significant practical and theoretical implications. Practically, it demonstrates the versatility of mutual information maximization paradigms for improved language representation, suggesting potentially vast landscapes for research in designing more robust self-supervised learning objectives. Theoretically, it elucidates how representation learning techniques across various domains converge towards similar foundational objectives, allowing insights and progress to be interdisciplinary.

Looking to the future, this framework opens up several directions for research. By providing a robust understanding of existing approaches under mutual information maximization, it suggests the exploration of more complex negative samples within the contrastive learning paradigm or the development of higher-order feature integration as future directions. Integrating syntactical and semantical vistas into view definitions, exploring different encoder architectures, and creating superior training objectives could foster advancements in AI and NLP.

The authors have laid a theoretical foundation that could propel research in designing novel representation learning strategies. By bridging various domains through mutual information maximization, the paper paves the way for unified advancements in self-supervised and contrastive learning.