  Word sense disambiguation (WSD) is a well researched problem in computational
linguistics. Different research works have approached this problem in different
ways. Some state of the art results that have been achieved for this problem
are by supervised models in terms of accuracy, but they often fall behind
flexible knowledge-based solutions which use engineered features as well as
human annotators to disambiguate every target word. This work focuses on
bridging this gap using neural sequence models incorporating the well-known
attention mechanism. The main gist of our work is to combine multiple
attentions on different linguistic features through weights and to provide a
unified framework for doing this. This weighted attention allows the model to
easily disambiguate the sense of an ambiguous word by attending over a suitable
portion of a sentence. Our extensive experiments show that multiple attention
enables a more versatile encoder-decoder model leading to state of the art
results.
