  Neural Machine Translation has lately gained a lot of "attention" with the
advent of more and more sophisticated but drastically improved models.
Attention mechanism has proved to be a boon in this direction by providing
weights to the input words, making it easy for the decoder to identify words
representing the present context. But by and by, as newer attention models with
more complexity came into development, they involved large computation, making
inference slow. In this paper, we have modelled the attention network using
techniques resonating with social choice theory. Along with that, the attention
mechanism, being a Markov Decision Process, has been represented by
reinforcement learning techniques. Thus, we propose to use an election method
($k$-Borda), fine-tuned using Q-learning, as a replacement for attention
networks. The inference time for this network is less than a standard Bahdanau
translator, and the results of the translation are comparable. This not only
experimentally verifies the claims stated above but also helped provide a
faster inference.
