  Modern sentence encoders are used to generate dense vector representations
that capture the underlying linguistic characteristics for a sequence of words,
including phrases, sentences, or paragraphs. These kinds of representations are
ideal for training a classifier for an end task such as sentiment analysis,
question answering and text classification. Different models have been proposed
to efficiently generate general purpose sentence representations to be used in
pretraining protocols. While averaging is the most commonly used efficient
sentence encoder, Discrete Cosine Transform (DCT) was recently proposed as an
alternative that captures the underlying syntactic characteristics of a given
text without compromising practical efficiency compared to averaging. However,
as with most other sentence encoders, the DCT sentence encoder was only
evaluated in English. To this end, we utilize DCT encoder to generate universal
sentence representation for different languages such as German, French, Spanish
and Russian. The experimental results clearly show the superior effectiveness
of DCT encoding in which consistent performance improvements are achieved over
strong baselines on multiple standardized datasets.
