  What makes a universal sentence encoder universal? The notion of a generic
encoder of text appears to be at odds with the inherent contextualization and
non-permanence of language use in a dynamic world. However, mapping sentences
into generic fixed-length vectors for downstream similarity and retrieval tasks
has been fruitful, particularly for multilingual applications. How do we manage
this dilemma? In this work we propose Contextual Lensing, a methodology for
inducing context-oriented universal sentence vectors. We break the construction
of universal sentence vectors into a core, variable length, sentence matrix
representation equipped with an adaptable `lens' from which fixed-length
vectors can be induced as a function of the lens context. We show that it is
possible to focus notions of language similarity into a small number of lens
parameters given a core universal matrix representation. For example, we
demonstrate the ability to encode translation similarity of sentences across
several languages into a single weight matrix, even when the core encoder has
not seen parallel data.
