  Several computational models have been developed to detect and analyze
dialect variation in recent years. Most of these models assume a predefined set
of geographical regions over which they detect and analyze dialectal variation.
However, dialect variation occurs at multiple levels of geographic resolution
ranging from cities within a state, states within a country, and between
countries across continents. In this work, we propose a model that enables
detection of dialectal variation at multiple levels of geographic resolution
obviating the need for a-priori definition of the resolution level. Our method
DialectGram, learns dialect-sensitive word embeddings while being agnostic of
the geographic resolution. Specifically it only requires one-time training and
enables analysis of dialectal variation at a chosen resolution post-hoc -- a
significant departure from prior models which need to be re-trained whenever
the pre-defined set of regions changes. Furthermore, DialectGram explicitly
models senses thus enabling one to estimate the proportion of each sense usage
in any given region. Finally, we quantitatively evaluate our model against
other baselines on a new evaluation dataset DialectSim (in English) and show
that DialectGram can effectively model linguistic variation.
