  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).
