The phrase based systems for machine translation are limited by the phrases that they see during the training. For highly inflected languages, it is uncommon to see all the forms of a word in the parallel corpora used during training. This problem is amplified for verbs in highly inflected languages where the correct form of the word depends on factors like gender, number and tense aspect. We propose a solution to augment the phrase table with all possible forms of a verb for improving the overall accuracy of the MT system. Our system makes use of simple stemmers and easily available monolingual data to generate new phrase table entries that cover the different variations seen for a verb. We report significant gains in BLEU for English to Hindi translation.