  Problems involving code-mixed language are often plagued by a lack of
resources and an absence of materials to perform sophisticated transfer
learning with. In this paper we describe our submission to the Sentimix
Hindi-English task involving sentiment classification of code-mixed texts, and
with an F1 score of 67.1%, we demonstrate that simple convolution and attention
may well produce reasonable results.
