This paper makes a simple increment to the state-of- the-art in sarcasm detection research. Existing ap- proaches are unable to capture subtle forms of con- text incongruity which lies at the heart of sarcasm. We explore if prior work can be enhanced using se- mantic similarity/discordance between word embed- dings. We augment word embedding-based features to four feature sets reported in the past. We ex- periment with four types of word embeddings, and observe an improvement in sarcasm detection, irre- spective of the word embedding used or the original feature set to which our features are augmented. For example, this augmentation results in an improve- ment in F-score of around 4% for three out of these four feature sets, and a minor degradation in case of the fourth, when word2vec embeddings are used. Fi- nally, a comparison of the four embeddings shows that word2vec and dependency weight-based fea- tures outperform LSA and GloVe, in terms of their benefit to sarcasm detection.