The word-level indexing in retrieval systems is often affected by phonetic transformations in words due to sandhi. String matching approaches often result in low precision results. Though this is not semantically meaningful it is lexically valid. Such tools are put to use by some of the existing systems (<cite>Krishna et al., 2016</cite>; Mittal, 2010 ) to obtain additional morphological or syntactic information about the sentences. This limits the scalability of <cite>those systems</cite>, as they cannot handle out of vocabulary words. Scalability of <cite>such systems</cite> is further restricted as the sentences often need to undergo linguistically involved preprocessing steps that lead to human in the loop processing. The systems by <cite>Krishna et al. (2016)</cite> and Krishna et al. (2017) assume that the parser by Goyal et al. (2012) , identifies all the possible candidate chunks. Our proposed model is built with precisely one purpose in mind, which is to predict the final word-forms in a given sequence.