  Word Representations form the core component for almost all advanced Natural
Language Processing (NLP) applications such as text mining, question-answering,
and text summarization, etc. Over the last two decades, immense research is
conducted to come up with one single model to solve all major NLP tasks. The
major problem currently is that there are a plethora of choices for different
NLP tasks. Thus for NLP practitioners, the task of choosing the right model to
be used itself becomes a challenge. Thus combining multiple pre-trained word
embeddings and forming meta embeddings has become a viable approach to improve
tackle NLP tasks. Meta embedding learning is a process of producing a single
word embedding from a given set of pre-trained input word embeddings. In this
paper, we propose to use Meta Embedding derived from few State-of-the-Art
(SOTA) models to efficiently tackle mainstream NLP tasks like classification,
semantic relatedness, and text similarity. We have compared both ensemble and
dynamic variants to identify an efficient approach. The results obtained show
that even the best State-of-the-Art models can be bettered. Thus showing us
that meta-embeddings can be used for several NLP tasks by harnessing the power
of several individual representations.
